diff --git a/.dockerignore b/.dockerignore
index 1c641e7a..0b495924 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -4,3 +4,4 @@ server/transformers
 server/flash-attention
 cmake-build-debug/
 cmake-build-release/
+Dockerfile*
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index ce1cdc33..c563fa27 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -21,9 +21,11 @@ jobs:
   build-and-push:
     outputs:
       docker_image: ${{ steps.final.outputs.docker_image }}
+      docker_volume: ${{ steps.final.outputs.docker_volume }}
       docker_devices: ${{ steps.final.outputs.docker_devices }}
       runs_on: ${{ steps.final.outputs.runs_on }}
       label: ${{ steps.final.outputs.label }}
+      extra_pytest: ${{ steps.final.outputs.extra_pytest }}
     concurrency:
       group: ${{ github.workflow }}-build-and-push-image-${{ inputs.hardware }}-${{ github.head_ref || github.run_id }}
       cancel-in-progress: true
@@ -44,32 +46,39 @@ jobs:
             cuda)
                 export dockerfile="Dockerfile"
                 export label_extension=""
+                export docker_volume="/mnt/cache"
                 export docker_devices=""
                 export runs_on="aws-g6-12xl-plus-priv-cache"
                 export platform=""
+                export extra_pytest=""
                 ;;
             rocm)
                 export dockerfile="Dockerfile_amd"
                 export label_extension="-rocm"
                 export docker_devices="/dev/kfd,/dev/dri"
-                # TODO Re-enable when they pass.
-                # export runs_on="amd-gpu-tgi"
-                export runs_on="ubuntu-latest"
+                export docker_volume="/mnt"
+                export runs_on="amd-gpu-runners"
                 export platform=""
+                export extra_pytest="-k test_flash_gemma_gptq_load"
                 ;;
             intel-xpu)
                 export dockerfile="Dockerfile_intel"
                 export label_extension="-intel-xpu"
                 export docker_devices=""
+                export docker_volume="/mnt/cache"
                 export runs_on="ubuntu-latest"
                 export platform="xpu"
+                export extra_pytest=""
                 ;;
             intel-cpu)
                 export dockerfile="Dockerfile_intel"
                 export label_extension="-intel-cpu"
-                export docker_devices=""
-                export runs_on="ubuntu-latest"
+                export docker_devices="none"
+                export docker_volume="/mnt/cache"
+                # export runs_on="ubuntu-latest"
+                export runs_on="aws-highmemory-32-plus-priv"
                 export platform="cpu"
+                export extra_pytest="-k test_flash_gemma_simple"
                 ;;
           esac
           echo $dockerfile
@@ -81,8 +90,10 @@ jobs:
           echo "DOCKERFILE=${dockerfile}" >> $GITHUB_ENV
           echo "LABEL=${label_extension}" >> $GITHUB_ENV
           echo "PLATFORM=${platform}" >> $GITHUB_ENV
+          echo "DOCKER_VOLUME=${docker_volume}" >> $GITHUB_ENV
           echo "DOCKER_DEVICES=${docker_devices}" >> $GITHUB_ENV
           echo "RUNS_ON=${runs_on}" >> $GITHUB_ENV
+          echo "EXTRA_PYTEST=${extra_pytest}" >> $GITHUB_ENV
           echo REGISTRY_MIRROR=$REGISTRY_MIRROR >> $GITHUB_ENV
       - name: Initialize Docker Buildx
         uses: docker/setup-buildx-action@v3
@@ -157,16 +168,18 @@ jobs:
         run: |
           echo "docker_image=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:sha-${{ env.GITHUB_SHA_SHORT}}${{ env.LABEL }}" >> "$GITHUB_OUTPUT"
           echo "docker_devices=${{ env.DOCKER_DEVICES }}" >> "$GITHUB_OUTPUT"
+          echo "docker_volume=${{ env.DOCKER_VOLUME }}" >> "$GITHUB_OUTPUT"
           echo "runs_on=${{ env.RUNS_ON }}" >> "$GITHUB_OUTPUT"
           echo "label=${{ env.LABEL }}" >> "$GITHUB_OUTPUT"
+          echo "extra_pytest=${{ env.EXTRA_PYTEST }}" >> "$GITHUB_OUTPUT"
   integration_tests:
     concurrency:
       group: ${{ github.workflow }}-${{ github.job }}-${{ needs.build-and-push.outputs.label }}-${{ github.head_ref || github.run_id }}
       cancel-in-progress: true
     needs: build-and-push
+    if: needs.build-and-push.outputs.runs_on != 'ubuntu-latest'
     runs-on:
       group: ${{ needs.build-and-push.outputs.runs_on }}
-    if: needs.build-and-push.outputs.runs_on != 'ubuntu-latest'
     env:
       PYTEST_FLAGS: ${{ (startsWith(github.ref, 'refs/tags/') || github.ref == 'refs/heads/main' || inputs.release-tests == true) && '--release' || '--release' }}
     steps:
@@ -177,15 +190,16 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - name: Install
         run: |
           make install-integration-tests
       - name: Run tests
         run: |
-          export DOCKER_VOLUME=/mnt/cache
+          export DOCKER_VOLUME=${{ needs.build-and-push.outputs.docker_volume }}
           export DOCKER_IMAGE=${{ needs.build-and-push.outputs.docker_image }}
           export DOCKER_DEVICES=${{ needs.build-and-push.outputs.docker_devices }}
+          export EXTRA_PYTEST="${{ needs.build-and-push.outputs.extra_pytest }}"
           export HF_TOKEN=${{ secrets.HF_TOKEN }}
           echo $DOCKER_IMAGE
-          pytest -s -vv integration-tests ${PYTEST_FLAGS}
+          pytest -s -vv integration-tests ${PYTEST_FLAGS} ${EXTRA_PYTEST}
diff --git a/Cargo.lock b/Cargo.lock
index 6796212f..5e85e384 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -133,7 +133,7 @@ checksum = "0ae92a5119aa49cdbcf6b9f893fe4e1d98b04ccbf82ee0584ad948a44a734dea"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -172,7 +172,7 @@ checksum = "16e62a023e7c117e27523144c5d2459f4397fcc3cab0085af8e2224f643a0193"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -183,7 +183,7 @@ checksum = "721cae7de5c34fbb2acd27e21e6d2cf7b886dce0c27388d46c4e6c47ea4318dd"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -205,9 +205,9 @@ dependencies = [
 
 [[package]]
 name = "autocfg"
-version = "1.3.0"
+version = "1.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0c4b4d0bd25bd0b74681c0ad21497610ce1b7c91b1022cd21c80c6fbdd9476b0"
+checksum = "ace50bade8e6234aa140d9a2f552bbee1db4d353f69b8217bc503490fc1a9f26"
 
 [[package]]
 name = "av1-grain"
@@ -316,12 +316,12 @@ dependencies = [
 
 [[package]]
 name = "axum"
-version = "0.7.6"
+version = "0.7.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8f43644eed690f5374f1af436ecd6aea01cd201f6fbdf0178adaf6907afb2cec"
+checksum = "504e3947307ac8326a5437504c517c4b56716c9d98fac0028c2acc7ca47d70ae"
 dependencies = [
  "async-trait",
- "axum-core 0.4.4",
+ "axum-core 0.4.5",
  "bytes",
  "futures-util",
  "http 1.1.0",
@@ -367,9 +367,9 @@ dependencies = [
 
 [[package]]
 name = "axum-core"
-version = "0.4.4"
+version = "0.4.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5e6b8ba012a258d63c9adfa28b9ddcf66149da6f986c5b5452e629d5ee64bf00"
+checksum = "09f2bd6146b97ae3359fa0cc6d6b376d9539582c7b4220f041a33ec24c226199"
 dependencies = [
  "async-trait",
  "bytes",
@@ -392,7 +392,7 @@ version = "0.16.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "bdad298231394729042d1f155b93f9fdf0b5ee1aea0b62404c4d7341f7d8fe08"
 dependencies = [
- "axum 0.7.6",
+ "axum 0.7.7",
  "futures-core",
  "futures-util",
  "http 1.1.0",
@@ -456,7 +456,7 @@ dependencies = [
  "regex",
  "rustc-hash",
  "shlex",
- "syn 2.0.77",
+ "syn 2.0.79",
  "which",
 ]
 
@@ -605,9 +605,9 @@ dependencies = [
 
 [[package]]
 name = "cc"
-version = "1.1.21"
+version = "1.1.22"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "07b1695e2c7e8fc85310cde85aeaab7e3097f593c91d209d3f9df76c928100f0"
+checksum = "9540e661f81799159abee814118cc139a2004b3a3aa3ea37724a1b66530b90e0"
 dependencies = [
  "jobserver",
  "libc",
@@ -704,7 +704,7 @@ dependencies = [
  "heck 0.5.0",
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -971,7 +971,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "scratch",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -988,7 +988,7 @@ checksum = "98532a60dedaebc4848cb2cba5023337cc9ea3af16a5b062633fabfd9f18fb60"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -1012,7 +1012,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "strsim",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -1023,7 +1023,7 @@ checksum = "d336a2a514f6ccccaa3e09b02d41d35330c07ddf03a62165fcec10bb561c7806"
 dependencies = [
  "darling_core",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -1053,7 +1053,7 @@ dependencies = [
  "darling",
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -1063,7 +1063,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "4abae7035bf79b9877b779505d8cf3749285b80c43941eda66604841889451dc"
 dependencies = [
  "derive_builder_core",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -1192,9 +1192,9 @@ checksum = "e8c02a5121d4ea3eb16a80748c74f5549a5665e4c21333c6098f283870fbdea6"
 
 [[package]]
 name = "fdeflate"
-version = "0.3.4"
+version = "0.3.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4f9bfee30e4dedf0ab8b422f03af778d9612b63f502710fc500a334ebe2de645"
+checksum = "d8090f921a24b04994d9929e204f50b498a33ea6ba559ffaa05e04f7ee7fb5ab"
 dependencies = [
  "simd-adler32",
 ]
@@ -1207,9 +1207,9 @@ checksum = "0ce7134b9999ecaf8bcd65542e436736ef32ddca1b3e06094cb6ec5755203b80"
 
 [[package]]
 name = "flate2"
-version = "1.0.33"
+version = "1.0.34"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "324a1be68054ef05ad64b861cc9eaf1d623d2d8cb25b4bf2cb9cdd902b4bf253"
+checksum = "a1b589b4dc103969ad3cf85c950899926ec64300a1a46d76c03a6072957036f0"
 dependencies = [
  "crc32fast",
  "miniz_oxide 0.8.0",
@@ -1338,7 +1338,7 @@ checksum = "87750cf4b7a4c0625b1529e4c543c2182106e4dedc60a2a6455e00d212c489ac"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -1864,7 +1864,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b23a0c8dfe501baac4adf6ebbfa6eddf8f0c07f56b058cc1288017e32397846c"
 dependencies = [
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -1884,7 +1884,7 @@ checksum = "c34819042dc3d3971c46c2190835914dfbe0c3c13f61449b2997f4e9722dfa60"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -2270,7 +2270,6 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b8a240ddb74feaf34a79a7add65a741f3167852fba007066dcac1ca548d89c08"
 dependencies = [
  "adler",
- "simd-adler32",
 ]
 
 [[package]]
@@ -2280,6 +2279,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e2d80299ef12ff69b16a84bb182e3b9df68b5a91574d3d4fa6e41b65deec4df1"
 dependencies = [
  "adler2",
+ "simd-adler32",
 ]
 
 [[package]]
@@ -2319,7 +2319,7 @@ checksum = "a7ce64b975ed4f123575d11afd9491f2e37bbd5813fbfbc0f09ae1fbddea74e0"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -2519,7 +2519,7 @@ checksum = "ed3955f1a9c7c0c15e092f9c887db08b1fc683305fdf6eb6684f22555355e202"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -2599,9 +2599,12 @@ dependencies = [
 
 [[package]]
 name = "once_cell"
-version = "1.19.0"
+version = "1.20.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3fdb12b2476b595f9358c5161aa467c2438859caa136dec86c26fdd2efe17b92"
+checksum = "82881c4be219ab5faaf2ad5e5e5ecdff8c66bd7402ca3160975c93b24961afd1"
+dependencies = [
+ "portable-atomic",
+]
 
 [[package]]
 name = "onig"
@@ -2654,7 +2657,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -2808,7 +2811,7 @@ dependencies = [
  "glob",
  "once_cell",
  "opentelemetry 0.21.0",
- "ordered-float 4.2.2",
+ "ordered-float 4.3.0",
  "percent-encoding",
  "rand",
  "thiserror",
@@ -2828,7 +2831,7 @@ dependencies = [
  "lazy_static",
  "once_cell",
  "opentelemetry 0.23.0",
- "ordered-float 4.2.2",
+ "ordered-float 4.3.0",
  "percent-encoding",
  "rand",
  "thiserror",
@@ -2851,9 +2854,9 @@ dependencies = [
 
 [[package]]
 name = "ordered-float"
-version = "4.2.2"
+version = "4.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4a91171844676f8c7990ce64959210cd2eaef32c2612c50f9fae9f8aaa6065a6"
+checksum = "44d501f1a72f71d3c063a6bbc8f7271fa73aa09fe5d6283b6571e2ed176a2537"
 dependencies = [
  "num-traits",
 ]
@@ -2937,7 +2940,7 @@ checksum = "2f38a4412a78282e09a2cf38d195ea5420d15ba0602cb375210efbc877243965"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -2988,22 +2991,22 @@ dependencies = [
 
 [[package]]
 name = "png"
-version = "0.17.13"
+version = "0.17.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "06e4b0d3d1312775e782c86c91a111aa1f910cbb65e1337f9975b5f9a554b5e1"
+checksum = "52f9d46a34a05a6a57566bc2bfae066ef07585a6e3fa30fbbdff5936380623f0"
 dependencies = [
  "bitflags 1.3.2",
  "crc32fast",
  "fdeflate",
  "flate2",
- "miniz_oxide 0.7.4",
+ "miniz_oxide 0.8.0",
 ]
 
 [[package]]
 name = "portable-atomic"
-version = "1.8.0"
+version = "1.9.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d30538d42559de6b034bc76fd6dd4c38961b1ee5c6c56e3808c50128fdbc22ce"
+checksum = "cc9c68a3f6da06753e9335d63e27f6b9754dd1920d941135b7ea8224f141adb2"
 
 [[package]]
 name = "powerfmt"
@@ -3027,7 +3030,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "479cf940fbbb3426c32c5d5176f62ad57549a0bb84773423ba8be9d089f5faba"
 dependencies = [
  "proc-macro2",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -3079,7 +3082,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "8021cf59c8ec9c432cfc2526ac6b8aa508ecaf29cd415f271b8406c1b851c3fd"
 dependencies = [
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -3119,7 +3122,7 @@ dependencies = [
  "prost 0.12.6",
  "prost-types",
  "regex",
- "syn 2.0.77",
+ "syn 2.0.79",
  "tempfile",
 ]
 
@@ -3146,7 +3149,7 @@ dependencies = [
  "itertools 0.12.1",
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -3205,7 +3208,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-macros-backend",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -3218,7 +3221,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-build-config",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -3402,9 +3405,9 @@ dependencies = [
 
 [[package]]
 name = "redox_syscall"
-version = "0.5.5"
+version = "0.5.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "62871f2d65009c0256aed1b9cfeeb8ac272833c404e13d53d400cd0dad7a2ac0"
+checksum = "9b6dfecf2c74bce2466cabf93f6664d6998a69eb21e39f4207930065b27b771f"
 dependencies = [
  "bitflags 2.6.0",
 ]
@@ -3422,14 +3425,14 @@ dependencies = [
 
 [[package]]
 name = "regex"
-version = "1.10.6"
+version = "1.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4219d74c6b67a3654a9fbebc4b419e22126d13d2f3c4a07ee0cb61ff79a79619"
+checksum = "38200e5ee88914975b69f657f0801b6f6dccafd44fd9326302a4aaeecfacb1d8"
 dependencies = [
  "aho-corasick",
  "memchr",
- "regex-automata 0.4.7",
- "regex-syntax 0.8.4",
+ "regex-automata 0.4.8",
+ "regex-syntax 0.8.5",
 ]
 
 [[package]]
@@ -3443,13 +3446,13 @@ dependencies = [
 
 [[package]]
 name = "regex-automata"
-version = "0.4.7"
+version = "0.4.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "38caf58cc5ef2fed281f89292ef23f6365465ed9a41b7a7754eb4e26496c92df"
+checksum = "368758f23274712b504848e9d5a6f010445cc8b87a7cdb4d7cbee666c1288da3"
 dependencies = [
  "aho-corasick",
  "memchr",
- "regex-syntax 0.8.4",
+ "regex-syntax 0.8.5",
 ]
 
 [[package]]
@@ -3460,9 +3463,9 @@ checksum = "f162c6dd7b008981e4d40210aca20b4bd0f9b60ca9271061b07f78537722f2e1"
 
 [[package]]
 name = "regex-syntax"
-version = "0.8.4"
+version = "0.8.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7a66a03ae7c801facd77a29370b4faec201768915ac14a721ba36f20bc9c209b"
+checksum = "2b15c43186be67a4fd63bee50d0303afffcef381492ebe2c5d87f324e1b8815c"
 
 [[package]]
 name = "reqwest"
@@ -3563,7 +3566,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rust-embed-utils",
- "syn 2.0.77",
+ "syn 2.0.79",
  "walkdir",
 ]
 
@@ -3686,9 +3689,9 @@ dependencies = [
 
 [[package]]
 name = "rustls-pki-types"
-version = "1.8.0"
+version = "1.9.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fc0a2ce646f8655401bb81e7927b812614bd5d91dbc968696be50603510fcaf0"
+checksum = "0e696e35370c65c9c541198af4543ccd580cf17fc25d8e05c5a242b202488c55"
 
 [[package]]
 name = "rustls-webpki"
@@ -3813,7 +3816,7 @@ checksum = "243902eda00fad750862fc144cea25caca5e20d615af0a81bee94ca738f1df1f"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -3840,9 +3843,9 @@ dependencies = [
 
 [[package]]
 name = "serde_spanned"
-version = "0.6.7"
+version = "0.6.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "eb5b1b31579f3811bf615c144393417496f152e12ac8b7663bf664f4a815306d"
+checksum = "87607cb1398ed59d48732e575a4c28a7a8ebf2454b964fe3f224f2afc07909e1"
 dependencies = [
  "serde",
 ]
@@ -4028,7 +4031,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustversion",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -4050,9 +4053,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.77"
+version = "2.0.79"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9f35bcdf61fd8e7be6caf75f429fdca8beb3ed76584befb503b1569faee373ed"
+checksum = "89132cd0bf050864e1d38dc3bbc07a0eb8e7530af26344d3d2bbbef83499f590"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -4152,9 +4155,9 @@ checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1"
 
 [[package]]
 name = "tempfile"
-version = "3.12.0"
+version = "3.13.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "04cbcdd0c794ebb0d4cf35e88edd2f7d2c4c3e9a5a6dab322839b321c6a87a64"
+checksum = "f0f2c9fc62d0beef6951ccffd757e241266a2c833136efbe35af6cd2567dca5b"
 dependencies = [
  "cfg-if",
  "fastrand",
@@ -4174,7 +4177,7 @@ dependencies = [
 
 [[package]]
 name = "text-generation-backends-trtllm"
-version = "2.3.1-dev0"
+version = "2.3.2-dev0"
 dependencies = [
  "async-stream",
  "async-trait",
@@ -4197,7 +4200,7 @@ dependencies = [
 
 [[package]]
 name = "text-generation-benchmark"
-version = "2.3.1-dev0"
+version = "2.3.2-dev0"
 dependencies = [
  "average",
  "clap 4.5.18",
@@ -4217,7 +4220,7 @@ dependencies = [
 
 [[package]]
 name = "text-generation-client"
-version = "2.3.1-dev0"
+version = "2.3.2-dev0"
 dependencies = [
  "async-trait",
  "base64 0.22.1",
@@ -4235,7 +4238,7 @@ dependencies = [
 
 [[package]]
 name = "text-generation-launcher"
-version = "2.3.1-dev0"
+version = "2.3.2-dev0"
 dependencies = [
  "clap 4.5.18",
  "ctrlc",
@@ -4244,6 +4247,7 @@ dependencies = [
  "nix 0.28.0",
  "once_cell",
  "pyo3",
+ "regex",
  "reqwest",
  "serde",
  "serde_json",
@@ -4255,11 +4259,11 @@ dependencies = [
 
 [[package]]
 name = "text-generation-router"
-version = "2.3.1-dev0"
+version = "2.3.2-dev0"
 dependencies = [
  "async-stream",
  "async-trait",
- "axum 0.7.6",
+ "axum 0.7.7",
  "axum-tracing-opentelemetry",
  "base64 0.22.1",
  "clap 4.5.18",
@@ -4304,11 +4308,11 @@ dependencies = [
 
 [[package]]
 name = "text-generation-router-v2"
-version = "2.3.1-dev0"
+version = "2.3.2-dev0"
 dependencies = [
  "async-stream",
  "async-trait",
- "axum 0.7.6",
+ "axum 0.7.7",
  "axum-tracing-opentelemetry",
  "base64 0.22.1",
  "clap 4.5.18",
@@ -4353,11 +4357,11 @@ dependencies = [
 
 [[package]]
 name = "text-generation-router-v3"
-version = "2.3.1-dev0"
+version = "2.3.2-dev0"
 dependencies = [
  "async-stream",
  "async-trait",
- "axum 0.7.6",
+ "axum 0.7.7",
  "axum-tracing-opentelemetry",
  "base64 0.22.1",
  "clap 4.5.18",
@@ -4428,7 +4432,7 @@ checksum = "08904e7672f5eb876eaaf87e0ce17857500934f4981c4a0ab2b4aa98baac7fc3"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -4533,7 +4537,7 @@ dependencies = [
  "rayon",
  "rayon-cond",
  "regex",
- "regex-syntax 0.8.4",
+ "regex-syntax 0.8.5",
  "serde",
  "serde_json",
  "spm_precompiled",
@@ -4566,7 +4570,7 @@ dependencies = [
  "rayon",
  "rayon-cond",
  "regex",
- "regex-syntax 0.8.4",
+ "regex-syntax 0.8.5",
  "serde",
  "serde_json",
  "spm_precompiled",
@@ -4612,7 +4616,7 @@ checksum = "693d596312e88961bc67d7f1f97af8a70227d9f90c31bba5806eec004978d752"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -4771,7 +4775,7 @@ dependencies = [
  "proc-macro2",
  "prost-build",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -4858,7 +4862,7 @@ checksum = "34704c8d6ebcbc939824180af020566b01a7c01f80641264eba0999f6c2b6be7"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -5151,7 +5155,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "regex",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -5160,7 +5164,7 @@ version = "6.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0b39868d43c011961e04b41623e050aedf2cc93652562ff7935ce0f819aaf2da"
 dependencies = [
- "axum 0.7.6",
+ "axum 0.7.7",
  "mime_guess",
  "regex",
  "rust-embed",
@@ -5189,7 +5193,7 @@ checksum = "ee1cd046f83ea2c4e920d6ee9f7c3537ef928d75dce5d84a87c2c5d6b3999a3a"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -5290,7 +5294,7 @@ dependencies = [
  "once_cell",
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
  "wasm-bindgen-shared",
 ]
 
@@ -5324,7 +5328,7 @@ checksum = "afc340c74d9005395cf9dd098506f7f44e38f2b4a21c6aaacf9a105ea5e1e836"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
  "wasm-bindgen-backend",
  "wasm-bindgen-shared",
 ]
@@ -5668,9 +5672,9 @@ checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec"
 
 [[package]]
 name = "winnow"
-version = "0.6.19"
+version = "0.6.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c52ac009d615e79296318c1bcce2d422aaca15ad08515e344feeda07df67a587"
+checksum = "36c1fec1a2bb5866f07c25f68c26e565c4c200aebb96d7e55710c19d3e8ac49b"
 dependencies = [
  "memchr",
 ]
@@ -5703,7 +5707,7 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.77",
+ "syn 2.0.79",
 ]
 
 [[package]]
diff --git a/Cargo.toml b/Cargo.toml
index a783fadb..ad2caeb8 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -20,7 +20,7 @@ default-members = [
 resolver = "2"
 
 [workspace.package]
-version = "2.3.1-dev0"
+version = "2.3.2-dev0"
 edition = "2021"
 authors = ["Olivier Dehaene"]
 homepage = "https://github.com/huggingface/text-generation-inference"
diff --git a/Dockerfile b/Dockerfile
index 80e5b681..daeb9309 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,5 +1,5 @@
 # Rust builder
-FROM lukemathwalker/cargo-chef:latest-rust-1.80 AS chef
+FROM lukemathwalker/cargo-chef:latest-rust-1.80.1 AS chef
 WORKDIR /usr/src
 
 ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
@@ -32,6 +32,7 @@ RUN cargo chef cook --profile release-opt --recipe-path recipe.json
 ARG GIT_SHA
 ARG DOCKER_LABEL
 
+COPY Cargo.lock Cargo.lock
 COPY Cargo.toml Cargo.toml
 COPY rust-toolchain.toml rust-toolchain.toml
 COPY proto proto
@@ -39,7 +40,7 @@ COPY benchmark benchmark
 COPY router router
 COPY backends backends
 COPY launcher launcher
-RUN cargo build --profile release-opt
+RUN cargo build --profile release-opt --frozen
 
 # Python builder
 # Adapted from: https://github.com/pytorch/pytorch/blob/master/Dockerfile
diff --git a/Dockerfile.nix b/Dockerfile.nix
new file mode 100644
index 00000000..f1e7e0f5
--- /dev/null
+++ b/Dockerfile.nix
@@ -0,0 +1,24 @@
+# Build the image and get out the docker file:
+#
+# docker build -t tgi-nix-builder -f Dockerfile.nix
+# docker run --log-driver=none tgi-nix-builder | docker load
+
+FROM nixos/nix:2.18.8 AS builder
+RUN echo "experimental-features = nix-command flakes" >> /etc/nix/nix.conf
+RUN nix profile install nixpkgs#cachix
+RUN cachix use text-generation-inference
+WORKDIR /root
+ADD . .
+RUN nix build .
+RUN mkdir /tmp/nix-store-closure
+RUN cp -R $(nix-store -qR result/) /tmp/nix-store-closure
+
+FROM ubuntu:24.04
+
+WORKDIR /app
+
+# Copy /nix/store
+COPY --from=builder /tmp/nix-store-closure /nix/store
+COPY --from=builder /root/result /app
+RUN ldconfig
+CMD ["ldconfig", "/app/bin/text-generation-launcher"]
diff --git a/Dockerfile_amd b/Dockerfile_amd
index a79aae48..4bb6407a 100644
--- a/Dockerfile_amd
+++ b/Dockerfile_amd
@@ -1,5 +1,5 @@
 # Rust builder
-FROM lukemathwalker/cargo-chef:latest-rust-1.80 AS chef
+FROM lukemathwalker/cargo-chef:latest-rust-1.80.1 AS chef
 WORKDIR /usr/src
 
 ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
@@ -31,6 +31,7 @@ RUN cargo chef cook --profile release-opt --recipe-path recipe.json
 ARG GIT_SHA
 ARG DOCKER_LABEL
 
+COPY Cargo.lock Cargo.lock
 COPY Cargo.toml Cargo.toml
 COPY rust-toolchain.toml rust-toolchain.toml
 COPY proto proto
@@ -38,10 +39,10 @@ COPY benchmark benchmark
 COPY router router
 COPY backends backends
 COPY launcher launcher
-RUN cargo build --profile release-opt
+RUN cargo build --profile release-opt --frozen
 
 # Text Generation Inference base image for RoCm
-FROM rocm/dev-ubuntu-22.04:6.1.1_hip_update AS base
+FROM rocm/dev-ubuntu-22.04:6.2 AS base
 
 RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
     build-essential \
@@ -50,33 +51,34 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-ins
     curl \
     git \
     make \
+    libmsgpack-dev \
     libssl-dev \
+    llvm-dev \
     g++ \
     # Needed to build VLLM & flash.
     rocthrust-dev \
     hipsparse-dev \
     hipblas-dev \
-    hipblaslt-dev \
+    hipcub-dev \
     rocblas-dev \
     hiprand-dev \
+    hipfft-dev \
     rocrand-dev \
     miopen-hip-dev \
-    hipfft-dev \
-    hipcub-dev \
     hipsolver-dev \
     rccl-dev \
     cmake \
-    python3.11-dev && \
+    python3.11-venv && \
     rm -rf /var/lib/apt/lists/*
 
 # Keep in sync with `server/pyproject.toml
 ARG MAMBA_VERSION=23.1.0-1
-ARG PYTORCH_VERSION='2.3.0'
-ARG ROCM_VERSION='6.0.2'
 ARG PYTHON_VERSION='3.11.10'
 # Automatically set by buildx
 ARG TARGETPLATFORM
-ENV PATH /opt/conda/bin:$PATH
+ENV PATH=/opt/conda/bin:$PATH
+
+ARG PYTORCH_ROCM_ARCH="gfx90a;gfx942"
 
 # TGI seem to require libssl.so.1.1 instead of libssl.so.3 so we can't use ubuntu 22.04. Ubuntu 20.04 has python==3.8, and TGI requires python>=3.9, hence the need for miniconda.
 # Install mamba
@@ -100,41 +102,132 @@ RUN case ${TARGETPLATFORM} in \
                          /opt/conda/bin/conda install -y "python=${PYTHON_VERSION}" ;; \
     esac && \
     /opt/conda/bin/conda clean -ya
+
 # Install flash-attention, torch dependencies
-RUN pip install numpy einops ninja --no-cache-dir
+RUN python3 -m pip install --upgrade pip && pip install numpy einops ninja joblib msgpack cmake --no-cache-dir && rm -rf /var/lib/apt/lists/*
 
-RUN pip uninstall -y triton && \
-    git clone --depth 1 --single-branch https://github.com/ROCm/triton.git && \
-    cd triton/python && \
-    pip install .
+RUN conda install mkl=2021
+ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib/:/opt/conda/lib/python3.11/site-packages/torch/lib:/opt/conda/lib/
 
-RUN git clone --depth 1 --recursive --single-branch --branch 2.3-patched https://github.com/fxmarty/pytorch.git pytorch && cd pytorch && pip install -r requirements.txt --no-cache-dir
 
-ARG _GLIBCXX_USE_CXX11_ABI="1"
-ARG CMAKE_PREFIX_PATH="/opt/conda"
+ARG COMMON_WORKDIR=/
+WORKDIR ${COMMON_WORKDIR}
+
+
+# Install HIPBLASLt
+FROM base AS build_hipblaslt
+ARG HIPBLASLT_BRANCH="e6da924"
+RUN git clone https://github.com/ROCm/hipBLASLt.git \
+    && cd hipBLASLt \
+    && git checkout ${HIPBLASLT_BRANCH} \
+    && SCCACHE_IDLE_TIMEOUT=1800 ./install.sh --architecture ${PYTORCH_ROCM_ARCH} --legacy_hipblas_direct \
+    && cd build/release \
+    && make package
+
+FROM scratch AS export_hipblaslt
+ARG COMMON_WORKDIR
+COPY --from=build_hipblaslt ${COMMON_WORKDIR}/hipBLASLt/build/release/*.deb /
+
+# RCCL build stages
+FROM base AS build_rccl
+ARG RCCL_BRANCH="rocm-6.2.0"
+RUN git clone https://github.com/ROCm/rccl \
+    && cd rccl \
+    && git checkout ${RCCL_BRANCH} \
+    && ./install.sh -p --amdgpu_targets ${PYTORCH_ROCM_ARCH}
+FROM scratch AS export_rccl
+ARG COMMON_WORKDIR
+COPY --from=build_rccl ${COMMON_WORKDIR}/rccl/build/release/*.deb /
+
+# Triton build stages
+FROM base AS build_triton
+ARG TRITON_BRANCH="e192dba"
+ARG TRITON_REPO="https://github.com/triton-lang/triton.git"
+RUN python3 -m pip install ninja cmake wheel pybind11 && git clone ${TRITON_REPO} \
+    && cd triton \
+    && git checkout ${TRITON_BRANCH} \
+    && cd python \
+    && python3 setup.py bdist_wheel --dist-dir=dist
+FROM scratch AS export_triton
+ARG COMMON_WORKDIR
+COPY --from=build_triton ${COMMON_WORKDIR}/triton/python/dist/*.whl /
+
+# # AMD-SMI build stages
+FROM base AS build_amdsmi
+RUN cd /opt/rocm/share/amd_smi \
+    && pip wheel . --wheel-dir=dist
+FROM scratch AS export_amdsmi
+COPY --from=build_amdsmi /opt/rocm/share/amd_smi/dist/*.whl /
+
+
+FROM base as build_pytorch
+
+RUN --mount=type=bind,from=export_hipblaslt,src=/,target=/install \
+    if ls /install/*.deb; then \
+        dpkg -i /install/*.deb \
+        && sed -i 's/, hipblaslt-dev \(.*\), hipcub-dev/, hipcub-dev/g' /var/lib/dpkg/status \
+        && sed -i 's/, hipblaslt \(.*\), hipfft/, hipfft/g' /var/lib/dpkg/status; \
+    fi
+
+ARG BUILD_ENVIRONMENT=pytorch-linux-jammy-rocm6.2-py3.11
 ARG PYTORCH_ROCM_ARCH="gfx90a;gfx942"
-ARG BUILD_CAFFE2="0" \
-    BUILD_CAFFE2_OPS="0" \
-    USE_CUDA="0" \
-    USE_ROCM="1" \
-    BUILD_TEST="0" \
-    USE_FBGEMM="0" \
-    USE_NNPACK="0" \
-    USE_QNNPACK="0" \
-    USE_XNNPACK="0" \
-    USE_FLASH_ATTENTION="1" \
-    USE_MEM_EFF_ATTENTION="0"
 
-RUN cd pytorch && python tools/amd_build/build_amd.py && python setup.py install
+# A commit to fix the output scaling factor issue in _scaled_mm
+# Not yet in 2.5.0-rc1
+ARG PYTORCH_BRANCH="cedc116"
+ARG PYTORCH_VISION_BRANCH="v0.19.1"
+ARG PYTORCH_REPO="https://github.com/ROCm/pytorch.git"
 
-# Set AS recommended: https://github.com/ROCm/triton/wiki/A-script-to-set-program-execution-environment-in-ROCm
-ENV HIP_FORCE_DEV_KERNARG=1
+RUN git clone ${PYTORCH_REPO} pytorch \
+    && cd pytorch && git checkout ${PYTORCH_BRANCH} && git submodule update --init --recursive \
+    && pip install -r requirements.txt --no-cache-dir  \
+    && python tools/amd_build/build_amd.py \
+    && CMAKE_PREFIX_PATH=$(python3 -c 'import sys; print(sys.prefix)') python3 setup.py bdist_wheel --dist-dir=dist
+FROM scratch as export_pytorch
+ARG COMMON_WORKDIR
+COPY --from=build_pytorch ${COMMON_WORKDIR}/pytorch/dist/*.whl /
 
-# On MI250 and MI300, performances for flash with Triton FA are slightly better than CK.
-# However, Triton requires a tunning for each prompt length, which is prohibitive.
-ENV ROCM_USE_FLASH_ATTN_V2_TRITON=0
+FROM base AS install_deps
 
-FROM base AS kernel-builder
+ARG COMMON_WORKDIR
+
+# Install hipblaslt
+RUN --mount=type=bind,from=export_hipblaslt,src=/,target=/install \
+    if ls /install/*.deb; then \
+        dpkg -i /install/*.deb \
+        && sed -i 's/, hipblaslt-dev \(.*\), hipcub-dev/, hipcub-dev/g' /var/lib/dpkg/status \
+        && sed -i 's/, hipblaslt \(.*\), hipfft/, hipfft/g' /var/lib/dpkg/status; \
+    fi
+
+RUN --mount=type=bind,from=export_rccl,src=/,target=/install \
+    if ls /install/*.deb; then \
+        dpkg -i /install/*.deb \
+        # RCCL needs to be installed twice
+        && dpkg -i /install/*.deb \
+        && sed -i 's/, rccl-dev \(.*\), rocalution/, rocalution/g' /var/lib/dpkg/status \
+        && sed -i 's/, rccl \(.*\), rocalution/, rocalution/g' /var/lib/dpkg/status; \
+    fi
+
+RUN --mount=type=bind,from=export_triton,src=/,target=/install \
+    if ls /install/*.whl; then \
+        # Preemptively uninstall to prevent pip same-version no-installs
+        pip uninstall -y triton \
+        && pip install /install/*.whl; \
+    fi
+
+RUN --mount=type=bind,from=export_amdsmi,src=/,target=/install \
+    # Preemptively uninstall to prevent pip same-version no-installs
+    pip uninstall -y amdsmi \
+    && pip install /install/*.whl;
+
+RUN --mount=type=bind,from=export_pytorch,src=/,target=/install \
+    if ls /install/*.whl; then \
+        # Preemptively uninstall to prevent pip same-version no-installs
+        pip uninstall -y torch torchvision \
+        && pip install /install/*.whl; \
+    fi
+
+FROM install_deps AS kernel-builder
 
 # # Build vllm kernels
 FROM kernel-builder AS vllm-builder
@@ -174,7 +267,7 @@ COPY server/exllamav2_kernels/ .
 
 RUN python setup.py build
 
-FROM base AS base-copy
+FROM install_deps AS base-copy
 
 # Text Generation Inference base env
 ENV HF_HOME=/data \
@@ -224,6 +317,19 @@ ENTRYPOINT ["./entrypoint.sh"]
 # Final image
 FROM base-copy
 
+# Set AS recommended: https://github.com/ROCm/triton/wiki/A-script-to-set-program-execution-environment-in-ROCm
+ENV HIP_FORCE_DEV_KERNARG=1
+
+# On MI250 and MI300, performances for flash with Triton FA are slightly better than CK.
+# However, Triton requires a tunning for each prompt length, which is prohibitive.
+ENV ROCM_USE_FLASH_ATTN_V2_TRITON=0
+ENV ROCM_USE_CUSTOM_PAGED_ATTN=1
+ENV PYTORCH_TUNABLEOP_TUNING_AFTER_WARMUP=0
+ENV VLLM_MOE_PADDING=0
+ENV ATTENTION=paged
+ENV USE_PREFIX_CACHING=0
+ENV ROCM_USE_SKINNY_GEMM=1
+
 COPY ./tgi-entrypoint.sh /tgi-entrypoint.sh
 RUN chmod +x /tgi-entrypoint.sh
 
diff --git a/Dockerfile_intel b/Dockerfile_intel
index 7ab6bba1..9b5dd20a 100644
--- a/Dockerfile_intel
+++ b/Dockerfile_intel
@@ -1,6 +1,6 @@
 ARG PLATFORM=xpu
 
-FROM lukemathwalker/cargo-chef:latest-rust-1.80 AS chef
+FROM lukemathwalker/cargo-chef:latest-rust-1.80.1 AS chef
 WORKDIR /usr/src
 
 ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
@@ -32,6 +32,7 @@ RUN cargo chef cook --profile release-opt --recipe-path recipe.json
 ARG GIT_SHA
 ARG DOCKER_LABEL
 
+COPY Cargo.lock Cargo.lock
 COPY Cargo.toml Cargo.toml
 COPY rust-toolchain.toml rust-toolchain.toml
 COPY proto proto
@@ -39,7 +40,7 @@ COPY benchmark benchmark
 COPY router router
 COPY backends backends
 COPY launcher launcher
-RUN cargo build --profile release-opt
+RUN cargo build --profile release-opt --frozen
 
 
 # Text Generation Inference base image for Intel
@@ -52,7 +53,7 @@ ARG MAMBA_VERSION=23.1.0-1
 ARG PYTHON_VERSION='3.11.10'
 # Automatically set by buildx
 ARG TARGETPLATFORM
-ENV PATH /opt/conda/bin:$PATH
+ENV PATH=/opt/conda/bin:$PATH
 
 # TGI seem to require libssl.so.1.1 instead of libssl.so.3 so we can't use ubuntu 22.04. Ubuntu 20.04 has python==3.8, and TGI requires python>=3.9, hence the need for miniconda.
 # Install mamba
@@ -111,6 +112,8 @@ ENV PATH=/opt/conda/bin:/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/bin:/opt/
 ENV CCL_ZE_IPC_EXCHANGE=sockets
 ENV CMAKE_PREFIX_PATH=/opt/intel/oneapi/mkl/latest/lib/cmake:/opt/intel/oneapi/compiler/latest
 ENV CPATH=/opt/intel/oneapi/mpi/latest/include:/opt/intel/oneapi/ccl/latest/include:/opt/intel/oneapi/mkl/latest/include
+ENV TORCH_LLM_ALLREDUCE=1
+ENV CCL_TOPO_FABRIC_VERTEX_CONNECTION_CHECK=0
 
 # Install benchmarker
 COPY --from=builder /usr/src/target/release-opt/text-generation-benchmark /usr/local/bin/text-generation-benchmark
@@ -127,12 +130,22 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-ins
     curl \
     ca-certificates \
     make \
-    g++ \
+    g++-12 \
+    gcc-12 \
     git \
     wget \
     cmake \
     libnuma-dev
 
+RUN update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-12 12
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 12
+RUN update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30
+RUN update-alternatives --set cc /usr/bin/gcc
+
+RUN update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30
+RUN update-alternatives --set c++ /usr/bin/g++
+
+
 ENV HUGGINGFACE_HUB_CACHE=/data \
     HF_HUB_ENABLE_HF_TRANSFER=1 \
     PORT=80
@@ -164,16 +177,17 @@ RUN case ${TARGETPLATFORM} in \
 
 RUN conda install -c conda-forge gperftools mkl
 
-RUN pip install https://download.pytorch.org/whl/nightly/cpu/torch-2.4.0.dev20240612%2Bcpu-cp311-cp311-linux_x86_64.whl
-RUN pip install https://download.pytorch.org/whl/nightly/cpu/torchvision-0.19.0.dev20240612%2Bcpu-cp311-cp311-linux_x86_64.whl
-RUN pip install https://download.pytorch.org/whl/nightly/cpu/torchaudio-2.4.0.dev20240612%2Bcpu-cp311-cp311-linux_x86_64.whl
+
+RUN pip install https://download.pytorch.org/whl/nightly/cpu/torch-2.5.0.dev20240815%2Bcpu-cp311-cp311-linux_x86_64.whl
+RUN pip install https://download.pytorch.org/whl/nightly/cpu/torchvision-0.20.0.dev20240815%2Bcpu-cp311-cp311-linux_x86_64.whl
+RUN pip install https://download.pytorch.org/whl/nightly/cpu/torchaudio-2.4.0.dev20240815%2Bcpu-cp311-cp311-linux_x86_64.whl
+
 RUN pip install triton py-libnuma
 
 WORKDIR /usr/src
 
-RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout eda7a7c42df6f9a64e0de9c2b69304ee02f2c32a
-
-RUN git clone https://github.com/intel/torch-ccl.git && cd torch-ccl && git checkout ccl_torch_dev_0131
+RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout f86e93e4890dc2c989024d148d415c9aa8a1649f
+RUN git clone https://github.com/intel/torch-ccl.git && cd torch-ccl && git checkout v2.4.0+cpu+rc0
 
 RUN cd intel-extension-for-pytorch && git submodule sync && git submodule update --init --recursive && python setup.py install
 
diff --git a/README.md b/README.md
index 156fa5cc..25dbbd43 100644
--- a/README.md
+++ b/README.md
@@ -83,7 +83,7 @@ model=HuggingFaceH4/zephyr-7b-beta
 volume=$PWD/data
 
 docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:2.3.0 --model-id $model
+    ghcr.io/huggingface/text-generation-inference:2.3.1 --model-id $model
 ```
 
 And then you can make requests like
@@ -120,7 +120,7 @@ curl localhost:3000/v1/chat/completions \
 
 **Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 12.2 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
 
-**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.2.0-rocm --model-id $model` instead of the command above.
+**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.3.1-rocm --model-id $model` instead of the command above.
 
 To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli):
 ```
@@ -150,7 +150,7 @@ model=meta-llama/Meta-Llama-3.1-8B-Instruct
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 token=<your cli READ token>
 
-docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $model
+docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.3.1 --model-id $model
 ```
 
 ### A note on Shared Memory (shm)
diff --git a/backends/v3/src/lib.rs b/backends/v3/src/lib.rs
index 77a9a11a..af66b21e 100644
--- a/backends/v3/src/lib.rs
+++ b/backends/v3/src/lib.rs
@@ -100,6 +100,7 @@ pub async fn connect_backend(
             .map_err(V3Error::Warmup)?,
     )?;
     tracing::info!("Setting max batch total tokens to {max_batch_total_tokens}");
+    metrics::gauge!("tgi_batch_max_total_tokens").set(max_batch_total_tokens);
 
     let backend_info = BackendInfo {
         waiting_served_ratio,
diff --git a/clients/python/pyproject.toml b/clients/python/pyproject.toml
index 2925085b..47ef9d71 100644
--- a/clients/python/pyproject.toml
+++ b/clients/python/pyproject.toml
@@ -27,3 +27,6 @@ asyncio_mode = "auto"
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
+
+[tool.isort]
+profile = "black"
diff --git a/clients/python/text_generation/types.py b/clients/python/text_generation/types.py
index f7f823fc..1085075e 100644
--- a/clients/python/text_generation/types.py
+++ b/clients/python/text_generation/types.py
@@ -28,11 +28,17 @@ class ToolCall(BaseModel):
     function: dict
 
 
+class Chunk(BaseModel):
+    type: str
+    text: Optional[str] = None
+    image_url: Any = None
+
+
 class Message(BaseModel):
     # Role of the message sender
     role: str
     # Content of the message
-    content: Optional[str] = None
+    content: Optional[Union[str, List[Chunk]]] = None
     # Optional name of the message sender
     name: Optional[str] = None
     # Tool calls associated with the chat completion
diff --git a/docs/openapi.json b/docs/openapi.json
index 0b5b3ae3..d1b60f4d 100644
--- a/docs/openapi.json
+++ b/docs/openapi.json
@@ -10,7 +10,7 @@
       "name": "Apache 2.0",
       "url": "https://www.apache.org/licenses/LICENSE-2.0"
     },
-    "version": "2.3.1-dev0"
+    "version": "2.3.2-dev0"
   },
   "paths": {
     "/": {
@@ -2114,12 +2114,18 @@
       "ToolType": {
         "oneOf": [
           {
-            "type": "object",
-            "default": null,
-            "nullable": true
+            "type": "string",
+            "description": "Means the model can pick between generating a message or calling one or more tools.",
+            "enum": [
+              "auto"
+            ]
           },
           {
-            "type": "string"
+            "type": "string",
+            "description": "Means the model will not call any tool and instead generates a message.",
+            "enum": [
+              "none"
+            ]
           },
           {
             "type": "object",
@@ -2131,13 +2137,10 @@
                 "$ref": "#/components/schemas/FunctionName"
               }
             }
-          },
-          {
-            "type": "object",
-            "default": null,
-            "nullable": true
           }
-        ]
+        ],
+        "description": "Controls which (if any) tool is called by the model.",
+        "example": "auto"
       },
       "Url": {
         "type": "object",
diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
index b883b36d..4876f7c5 100644
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -3,6 +3,8 @@
     title: Text Generation Inference
   - local: quicktour
     title: Quick Tour
+  - local: supported_models
+    title: Supported Models
   - local: installation_nvidia
     title: Using TGI with Nvidia GPUs
   - local: installation_amd
@@ -15,8 +17,7 @@
     title: Using TGI with Intel GPUs
   - local: installation
     title: Installation from source
-  - local: supported_models
-    title: Supported Models and Hardware
+
   - local: architecture
     title: Internal Architecture
   - local: usage_statistics
diff --git a/docs/source/architecture.md b/docs/source/architecture.md
index 28c84f62..6660630d 100644
--- a/docs/source/architecture.md
+++ b/docs/source/architecture.md
@@ -10,7 +10,7 @@ This diagram shows well there are these separate components:
 
 - **The router**, also named `webserver`, that receives the client requests, buffers them, creates some batches, and prepares gRPC calls to a model server.
 - **The model server**, responsible of receiving the gRPC requests and to process the inference on the model. If the model is sharded across multiple accelerators (e.g.: multiple GPUs), the model server shards might be synchronized via NCCL or equivalent.
-- **The launcher** is a helper thar will be able to launch one or several model servers (if model is sharded), and it launches the router with the compatible arguments.
+- **The launcher** is a helper that will be able to launch one or several model servers (if model is sharded), and it launches the router with the compatible arguments.
 
 The router and the model server can be two different machines, they do not need to be deployed together.
 
diff --git a/docs/source/basic_tutorials/gated_model_access.md b/docs/source/basic_tutorials/gated_model_access.md
index ef3a1db7..cf198dbe 100644
--- a/docs/source/basic_tutorials/gated_model_access.md
+++ b/docs/source/basic_tutorials/gated_model_access.md
@@ -19,6 +19,6 @@ docker run --gpus all \
     --shm-size 1g \
     -e HF_TOKEN=$token \
     -p 8080:80 \
-    -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0.4 \
+    -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.3.1 \
     --model-id $model
 ```
diff --git a/docs/source/conceptual/lora.md b/docs/source/conceptual/lora.md
index 0b7e3616..d1f4ce78 100644
--- a/docs/source/conceptual/lora.md
+++ b/docs/source/conceptual/lora.md
@@ -36,6 +36,12 @@ To use LoRA in TGI, when starting the server, you can specify the list of LoRA m
 LORA_ADAPTERS=predibase/customer_support,predibase/dbpedia
 ```
 
+To specify model revision, use `adapter_id@revision`, as follows:
+
+```bash
+LORA_ADAPTERS=predibase/customer_support@main,predibase/dbpedia@rev2
+```
+
 To use a locally stored lora adapter, use `adapter-name=/path/to/adapter`, as seen below. When you want to use this adapter, set `"parameters": {"adapter_id": "adapter-name"}"`
 
 ```bash
diff --git a/docs/source/conceptual/quantization.md b/docs/source/conceptual/quantization.md
index b7672a9f..1898b10c 100644
--- a/docs/source/conceptual/quantization.md
+++ b/docs/source/conceptual/quantization.md
@@ -19,7 +19,7 @@ bitsandbytes is a library used to apply 8-bit and 4-bit quantization to models.
 In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
 
 ```bash
-docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize bitsandbytes
+docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.3.1 --model-id $model --quantize bitsandbytes
 ```
 
 4-bit quantization is also possible with bitsandbytes. You can choose one of the following 4-bit data types: 4-bit float (`fp4`), or 4-bit `NormalFloat` (`nf4`). These data types were introduced in the context of parameter-efficient fine-tuning, but you can apply them for inference by automatically converting the model weights on load.
@@ -27,7 +27,7 @@ docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingf
 In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇
 
 ```bash
-docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize bitsandbytes-nf4
+docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.3.1 --model-id $model --quantize bitsandbytes-nf4
 ```
 
 You can get more information about 8-bit quantization by reading this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), and 4-bit quantization by reading [this blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).
@@ -48,7 +48,7 @@ $$({\hat{W}_{l}}^{*} = argmin_{\hat{W_{l}}} ||W_{l}X-\hat{W}_{l}X||^{2}_{2})$$
 TGI allows you to both run an already GPTQ quantized model (see available models [here](https://huggingface.co/models?search=gptq)) or quantize a model of your choice using quantization script. You can run a quantized model by simply passing --quantize like below 👇
 
 ```bash
-docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize gptq
+docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.3.1 --model-id $model --quantize gptq
 ```
 
 Note that TGI's GPTQ implementation doesn't use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) under the hood. However, models quantized using AutoGPTQ or Optimum can still be served by TGI.
diff --git a/docs/source/installation_amd.md b/docs/source/installation_amd.md
index 544beffc..86d092eb 100644
--- a/docs/source/installation_amd.md
+++ b/docs/source/installation_amd.md
@@ -11,7 +11,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 docker run --rm -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
     --device=/dev/kfd --device=/dev/dri --group-add video \
     --ipc=host --shm-size 256g --net host -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:2.3.0-rocm \
+    ghcr.io/huggingface/text-generation-inference:2.3.1-rocm \
     --model-id $model
 ```
 
@@ -31,6 +31,12 @@ Two implementations of Flash Attention are available for ROCm, the first is [ROC
 
 By default, the Composable Kernel implementation is used. However, the Triton implementation has slightly lower latency on MI250 and MI300, but requires a warmup which can be prohibitive as it needs to be done again for each new prompt length. If needed, FA Triton impelmentation can be enabled with `--env ROCM_USE_FLASH_ATTN_V2_TRITON="0"` when launching TGI's docker container.
 
+## Custom PagedAttention
+
+For better performance on ROCm, a custom Paged Attention kernel is available and is enabled by default. To disable it and fall back to the PagedAttention v2 kernel, set the environment variable `ROCM_USE_CUSTOM_PAGED_ATTN=0`.
+
+The custom kernel supports bf16 and fp16 data types, block size of 16, head size of 128, a maximum context length of 16k, and GQA ratios between 1 and 16. For other configurations, we use the PagedAttention v2 kernel.
+
 ## Unsupported features
 
 The following features are currently not supported in the ROCm version of TGI, and the supported may be extended in the future:
diff --git a/docs/source/installation_intel.md b/docs/source/installation_intel.md
index fcbe550f..1435b331 100644
--- a/docs/source/installation_intel.md
+++ b/docs/source/installation_intel.md
@@ -12,7 +12,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 docker run --rm --privileged --cap-add=sys_nice \
     --device=/dev/dri \
     --ipc=host --shm-size 1g --net host -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:2.3.0-intel-xpu \
+    ghcr.io/huggingface/text-generation-inference:2.3.1-intel-xpu \
     --model-id $model --cuda-graphs 0
 ```
 
@@ -29,7 +29,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 docker run --rm --privileged --cap-add=sys_nice \
     --device=/dev/dri \
     --ipc=host --shm-size 1g --net host -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:2.3.0-intel-cpu \
+    ghcr.io/huggingface/text-generation-inference:2.3.1-intel-cpu \
     --model-id $model --cuda-graphs 0
 ```
 
diff --git a/docs/source/installation_nvidia.md b/docs/source/installation_nvidia.md
index 8aa7a85a..634380fc 100644
--- a/docs/source/installation_nvidia.md
+++ b/docs/source/installation_nvidia.md
@@ -11,7 +11,7 @@ model=teknium/OpenHermes-2.5-Mistral-7B
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
 docker run --gpus all --shm-size 64g -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:2.3.0 \
+    ghcr.io/huggingface/text-generation-inference:2.3.1 \
     --model-id $model
 ```
 
diff --git a/docs/source/quicktour.md b/docs/source/quicktour.md
index 33832964..50363e5d 100644
--- a/docs/source/quicktour.md
+++ b/docs/source/quicktour.md
@@ -11,14 +11,13 @@ model=teknium/OpenHermes-2.5-Mistral-7B
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
 docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:2.3.0 \
+    ghcr.io/huggingface/text-generation-inference:2.3.1 \
     --model-id $model
 ```
 
 <Tip>
 
-If you want to serve gated or private models, which provide
-controlled access to sensitive or proprietary content, refer to
+If you want to serve gated or private models, please refer to
 [this guide](https://huggingface.co/docs/text-generation-inference/en/basic_tutorials/gated_model_access)
 for detailed instructions.
 
@@ -97,7 +96,7 @@ curl 127.0.0.1:8080/generate \
 To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
 
 ```bash
-docker run ghcr.io/huggingface/text-generation-inference:2.2.0 --help
+docker run ghcr.io/huggingface/text-generation-inference:2.3.1 --help
 ```
 
 </Tip>
diff --git a/docs/source/reference/launcher.md b/docs/source/reference/launcher.md
index c8d2a4c6..b1abd1ee 100644
--- a/docs/source/reference/launcher.md
+++ b/docs/source/reference/launcher.md
@@ -89,6 +89,15 @@ Options:
           [env: DTYPE=]
           [possible values: float16, bfloat16]
 
+```
+## KV_CACHE_DTYPE
+```shell
+      --kv-cache-dtype <KV_CACHE_DTYPE>
+          Specify the dtype for the key-value cache. When this option is not provided, the dtype of the model is used (typically `float16` or `bfloat16`). Currently the only supported value is `fp8_e5m2` on CUDA
+          
+          [env: KV_CACHE_DTYPE=]
+          [possible values: fp8_e5m2]
+
 ```
 ## TRUST_REMOTE_CODE
 ```shell
diff --git a/docs/source/supported_models.md b/docs/source/supported_models.md
index 832f88ef..28008bcd 100644
--- a/docs/source/supported_models.md
+++ b/docs/source/supported_models.md
@@ -1,9 +1,7 @@
 
-# Supported Models and Hardware
+# Supported Models
 
-Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models (VLMs & LLMs) are supported.
-
-## Supported Models
+Text Generation Inference enables serving optimized models. The following sections list which models (VLMs & LLMs) are supported.
 
 - [Deepseek V2](https://huggingface.co/deepseek-ai/DeepSeek-V2)
 - [Idefics 2](https://huggingface.co/HuggingFaceM4/idefics2-8b) (Multimodal)
@@ -20,6 +18,7 @@ Text Generation Inference enables serving optimized models on specific hardware
 - [Mixtral](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1)
 - [Gpt Bigcode](https://huggingface.co/bigcode/gpt_bigcode-santacoder)
 - [Phi](https://huggingface.co/microsoft/phi-1_5)
+- [PhiMoe](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct)
 - [Baichuan](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat)
 - [Falcon](https://huggingface.co/tiiuae/falcon-7b-instruct)
 - [StarCoder 2](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1)
@@ -34,6 +33,8 @@ Text Generation Inference enables serving optimized models on specific hardware
 - [Gpt Neox](https://huggingface.co/EleutherAI/gpt-neox-20b)
 - [Gptj](https://huggingface.co/EleutherAI/gpt-j-6b)
 - [Idefics](https://huggingface.co/HuggingFaceM4/idefics-9b) (Multimodal)
+- [Mllama](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) (Multimodal)
+
 
 
 If the above list lacks the model you would like to serve, depending on the model's pipeline type, you can try to initialize and serve the model anyways to see how well it performs, but performance isn't guaranteed for non-optimized models:
diff --git a/flake.lock b/flake.lock
index 14e23b77..aacdd30e 100644
--- a/flake.lock
+++ b/flake.lock
@@ -497,11 +497,11 @@
         "systems": "systems_7"
       },
       "locked": {
-        "lastModified": 1710146030,
-        "narHash": "sha256-SZ5L6eA7HJ/nmkzGG7/ISclqe6oZdOZTNoesiInkXPQ=",
+        "lastModified": 1726560853,
+        "narHash": "sha256-X6rJYSESBVr3hBoH0WbKE5KvhPU5bloyZ2L4K60/fPQ=",
         "owner": "numtide",
         "repo": "flake-utils",
-        "rev": "b1d9ab70662946ef0850d488da1c9019f3a9752a",
+        "rev": "c1dfcf08411b08f6b8615f7d8971a2bfa81d5e8a",
         "type": "github"
       },
       "original": {
@@ -718,11 +718,11 @@
     },
     "nixpkgs_6": {
       "locked": {
-        "lastModified": 1724915739,
-        "narHash": "sha256-7PgRge4mn5akFvhPwefuaLQGbF5BnmxlwZJEf7CgbrE=",
+        "lastModified": 1727675176,
+        "narHash": "sha256-xIjBFMYldWvj+g8ahxMPofsj+OqxvKJN6YylNHQ7gn4=",
         "owner": "nixos",
         "repo": "nixpkgs",
-        "rev": "85be051bb60943d3328d91aaf2598798f87e19af",
+        "rev": "a6d0207fea9212d28cd3d487efe6bc699663b93a",
         "type": "github"
       },
       "original": {
@@ -853,11 +853,11 @@
         ]
       },
       "locked": {
-        "lastModified": 1726626348,
-        "narHash": "sha256-sYV7e1B1yLcxo8/h+/hTwzZYmaju2oObNiy5iRI0C30=",
+        "lastModified": 1727836133,
+        "narHash": "sha256-JE0zciM5IGWvK8J/pE2VldNBf7oyMH5WrU8tZArefbg=",
         "owner": "oxalica",
         "repo": "rust-overlay",
-        "rev": "6fd52ad8bd88f39efb2c999cc971921c2fb9f3a2",
+        "rev": "02321540b0c8000b36889b1b974d1fec585b25a4",
         "type": "github"
       },
       "original": {
@@ -978,11 +978,11 @@
         "nixpkgs": "nixpkgs_6"
       },
       "locked": {
-        "lastModified": 1727353315,
-        "narHash": "sha256-yZovq/6P8Z199r7e+NbTXyCqRgK6grRkLxYHWHnHckI=",
+        "lastModified": 1728381423,
+        "narHash": "sha256-gpHy1WtlA8ZTd8XmxsdCoDd4Z7DE7co37lH7P+nsADA=",
         "owner": "huggingface",
         "repo": "text-generation-inference-nix",
-        "rev": "1d42c4125ebafb87707118168995675cc5050b9d",
+        "rev": "93123736c97e9f7bfe825bfaf3d7de0fc9a21a1e",
         "type": "github"
       },
       "original": {
diff --git a/flake.nix b/flake.nix
index 1b396453..edef442f 100644
--- a/flake.nix
+++ b/flake.nix
@@ -37,6 +37,7 @@
           overlays = [
             rust-overlay.overlays.default
             tgi-nix.overlays.default
+            (import nix/overlay.nix)
           ];
         };
         crateOverrides = import ./nix/crate-overrides.nix { inherit pkgs nix-filter; };
@@ -141,15 +142,26 @@
           };
         };
 
-        packages.default = pkgs.writeShellApplication {
-          name = "text-generation-inference";
-          runtimeInputs = [
-            server
-            router
-          ];
-          text = ''
-            ${launcher}/bin/text-generation-launcher "$@"
-          '';
+        packages = rec {
+          default = pkgs.writeShellApplication {
+            name = "text-generation-inference";
+            runtimeInputs = [
+              server
+              router
+            ];
+            text = ''
+              ${launcher}/bin/text-generation-launcher "$@"
+            '';
+          };
+
+          dockerImage = pkgs.callPackage nix/docker.nix {
+            text-generation-inference = default;
+          };
+
+          dockerImageStreamed = pkgs.callPackage nix/docker.nix {
+            text-generation-inference = default;
+            stream = true;
+          };
         };
       }
     );
diff --git a/integration-tests/conftest.py b/integration-tests/conftest.py
index eb55ebb9..f24fc079 100644
--- a/integration-tests/conftest.py
+++ b/integration-tests/conftest.py
@@ -336,6 +336,7 @@ def launcher(event_loop):
         use_flash_attention: bool = True,
         disable_grammar_support: bool = False,
         dtype: Optional[str] = None,
+        kv_cache_dtype: Optional[str] = None,
         revision: Optional[str] = None,
         max_input_length: Optional[int] = None,
         max_batch_prefill_tokens: Optional[int] = None,
@@ -375,6 +376,9 @@ def launcher(event_loop):
         if dtype is not None:
             args.append("--dtype")
             args.append(dtype)
+        if kv_cache_dtype is not None:
+            args.append("--kv-cache-dtype")
+            args.append(kv_cache_dtype)
         if revision is not None:
             args.append("--revision")
             args.append(revision)
@@ -434,6 +438,7 @@ def launcher(event_loop):
         use_flash_attention: bool = True,
         disable_grammar_support: bool = False,
         dtype: Optional[str] = None,
+        kv_cache_dtype: Optional[str] = None,
         revision: Optional[str] = None,
         max_input_length: Optional[int] = None,
         max_batch_prefill_tokens: Optional[int] = None,
@@ -456,6 +461,9 @@ def launcher(event_loop):
         if dtype is not None:
             args.append("--dtype")
             args.append(dtype)
+        if kv_cache_dtype is not None:
+            args.append("--kv-cache-dtype")
+            args.append(kv_cache_dtype)
         if revision is not None:
             args.append("--revision")
             args.append(revision)
@@ -484,6 +492,7 @@ def launcher(event_loop):
         try:
             container = client.containers.get(container_name)
             container.stop()
+            container.remove()
             container.wait()
         except NotFound:
             pass
@@ -506,13 +515,28 @@ def launcher(event_loop):
             volumes = [f"{DOCKER_VOLUME}:/data"]
 
         if DOCKER_DEVICES:
-            devices = DOCKER_DEVICES.split(",")
+            if DOCKER_DEVICES.lower() == "none":
+                devices = []
+            else:
+                devices = DOCKER_DEVICES.strip().split(",")
             visible = os.getenv("ROCR_VISIBLE_DEVICES")
             if visible:
                 env["ROCR_VISIBLE_DEVICES"] = visible
             device_requests = []
+            if not devices:
+                devices = None
+            elif devices == ["nvidia.com/gpu=all"]:
+                devices = None
+                device_requests = [
+                    docker.types.DeviceRequest(
+                        driver="cdi",
+                        # count=gpu_count,
+                        device_ids=[f"nvidia.com/gpu={i}"],
+                    )
+                    for i in range(gpu_count)
+                ]
         else:
-            devices = []
+            devices = None
             device_requests = [
                 docker.types.DeviceRequest(count=gpu_count, capabilities=[["gpu"]])
             ]
@@ -532,21 +556,26 @@ def launcher(event_loop):
             shm_size="1G",
         )
 
-        yield ContainerLauncherHandle(client, container.name, port)
-
-        if not use_flash_attention:
-            del env["USE_FLASH_ATTENTION"]
-
         try:
-            container.stop()
-            container.wait()
-        except NotFound:
-            pass
+            yield ContainerLauncherHandle(client, container.name, port)
 
-        container_output = container.logs().decode("utf-8")
-        print(container_output, file=sys.stderr)
+            if not use_flash_attention:
+                del env["USE_FLASH_ATTENTION"]
 
-        container.remove()
+            try:
+                container.stop()
+                container.wait()
+            except NotFound:
+                pass
+
+            container_output = container.logs().decode("utf-8")
+            print(container_output, file=sys.stderr)
+
+        finally:
+            try:
+                container.remove()
+            except Exception:
+                pass
 
     if DOCKER_IMAGE is not None:
         return docker_launcher
@@ -589,7 +618,6 @@ def generate_multi():
         max_new_tokens: int,
         seed: Optional[int] = None,
     ) -> List[Response]:
-
         import numpy as np
 
         arange = np.arange(len(prompts))
diff --git a/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma.json b/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_simple.json
similarity index 100%
rename from integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma.json
rename to integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_simple.json
diff --git a/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama.json b/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_simple.json
similarity index 100%
rename from integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama.json
rename to integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_simple.json
diff --git a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json
new file mode 100644
index 00000000..c55dd593
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json
@@ -0,0 +1,104 @@
+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 10,
+    "prefill": [
+      {
+        "id": 128000,
+        "logprob": null,
+        "text": "<|begin_of_text|>"
+      },
+      {
+        "id": 3923,
+        "logprob": -5.6328125,
+        "text": "What"
+      },
+      {
+        "id": 374,
+        "logprob": -1.2265625,
+        "text": " is"
+      },
+      {
+        "id": 5655,
+        "logprob": -9.1015625,
+        "text": " deep"
+      },
+      {
+        "id": 6975,
+        "logprob": -1.8085938,
+        "text": " learning"
+      },
+      {
+        "id": 30,
+        "logprob": -1.0439453,
+        "text": "?"
+      }
+    ],
+    "seed": null,
+    "tokens": [
+      {
+        "id": 18682,
+        "logprob": -2.1992188,
+        "special": false,
+        "text": " Deep"
+      },
+      {
+        "id": 6975,
+        "logprob": -0.079956055,
+        "special": false,
+        "text": " learning"
+      },
+      {
+        "id": 374,
+        "logprob": -0.2763672,
+        "special": false,
+        "text": " is"
+      },
+      {
+        "id": 264,
+        "logprob": -0.37548828,
+        "special": false,
+        "text": " a"
+      },
+      {
+        "id": 27084,
+        "logprob": -1.4628906,
+        "special": false,
+        "text": " subset"
+      },
+      {
+        "id": 315,
+        "logprob": -0.02885437,
+        "special": false,
+        "text": " of"
+      },
+      {
+        "id": 5780,
+        "logprob": -0.2565918,
+        "special": false,
+        "text": " machine"
+      },
+      {
+        "id": 6975,
+        "logprob": -0.0063438416,
+        "special": false,
+        "text": " learning"
+      },
+      {
+        "id": 430,
+        "logprob": -1.3056641,
+        "special": false,
+        "text": " that"
+      },
+      {
+        "id": 374,
+        "logprob": -1.6035156,
+        "special": false,
+        "text": " is"
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": " Deep learning is a subset of machine learning that is"
+}
diff --git a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json
new file mode 100644
index 00000000..d06d6e56
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json
@@ -0,0 +1,57 @@
+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "eos_token",
+    "generated_tokens": 3,
+    "prefill": [
+      {
+        "id": 128000,
+        "logprob": null,
+        "text": "<|begin_of_text|>"
+      },
+      {
+        "id": 374,
+        "logprob": -22.96875,
+        "text": " is"
+      },
+      {
+        "id": 5655,
+        "logprob": -10.71875,
+        "text": " deep"
+      },
+      {
+        "id": 6975,
+        "logprob": -2.6992188,
+        "text": " learning"
+      },
+      {
+        "id": 30,
+        "logprob": -4.8398438,
+        "text": "?"
+      }
+    ],
+    "seed": 0,
+    "tokens": [
+      {
+        "id": 720,
+        "logprob": -0.4411621,
+        "special": false,
+        "text": " \n"
+      },
+      {
+        "id": 220,
+        "logprob": -0.35864258,
+        "special": false,
+        "text": " "
+      },
+      {
+        "id": 128001,
+        "logprob": 0.0,
+        "special": true,
+        "text": "<|end_of_text|>"
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": "What is deep learning? \n "
+}
diff --git a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json
new file mode 100644
index 00000000..46670819
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json
@@ -0,0 +1,418 @@
+[
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 128000,
+          "logprob": null,
+          "text": "<|begin_of_text|>"
+        },
+        {
+          "id": 3923,
+          "logprob": -5.6328125,
+          "text": "What"
+        },
+        {
+          "id": 374,
+          "logprob": -1.2265625,
+          "text": " is"
+        },
+        {
+          "id": 5655,
+          "logprob": -9.1015625,
+          "text": " deep"
+        },
+        {
+          "id": 6975,
+          "logprob": -1.8085938,
+          "text": " learning"
+        },
+        {
+          "id": 30,
+          "logprob": -1.0439453,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 18682,
+          "logprob": -2.1992188,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 6975,
+          "logprob": -0.07897949,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 374,
+          "logprob": -0.27734375,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 264,
+          "logprob": -0.37402344,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 27084,
+          "logprob": -1.4511719,
+          "special": false,
+          "text": " subset"
+        },
+        {
+          "id": 315,
+          "logprob": -0.02909851,
+          "special": false,
+          "text": " of"
+        },
+        {
+          "id": 5780,
+          "logprob": -0.25854492,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 6975,
+          "logprob": -0.0061798096,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 430,
+          "logprob": -1.3046875,
+          "special": false,
+          "text": " that"
+        },
+        {
+          "id": 374,
+          "logprob": -1.5537109,
+          "special": false,
+          "text": " is"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": " Deep learning is a subset of machine learning that is"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 128000,
+          "logprob": null,
+          "text": "<|begin_of_text|>"
+        },
+        {
+          "id": 3923,
+          "logprob": -5.6328125,
+          "text": "What"
+        },
+        {
+          "id": 374,
+          "logprob": -1.2265625,
+          "text": " is"
+        },
+        {
+          "id": 5655,
+          "logprob": -9.1015625,
+          "text": " deep"
+        },
+        {
+          "id": 6975,
+          "logprob": -1.8085938,
+          "text": " learning"
+        },
+        {
+          "id": 30,
+          "logprob": -1.0439453,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 18682,
+          "logprob": -2.1992188,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 6975,
+          "logprob": -0.07897949,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 374,
+          "logprob": -0.27734375,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 264,
+          "logprob": -0.37402344,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 27084,
+          "logprob": -1.4511719,
+          "special": false,
+          "text": " subset"
+        },
+        {
+          "id": 315,
+          "logprob": -0.02909851,
+          "special": false,
+          "text": " of"
+        },
+        {
+          "id": 5780,
+          "logprob": -0.25854492,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 6975,
+          "logprob": -0.0061798096,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 430,
+          "logprob": -1.3046875,
+          "special": false,
+          "text": " that"
+        },
+        {
+          "id": 374,
+          "logprob": -1.5537109,
+          "special": false,
+          "text": " is"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": " Deep learning is a subset of machine learning that is"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 128000,
+          "logprob": null,
+          "text": "<|begin_of_text|>"
+        },
+        {
+          "id": 3923,
+          "logprob": -5.6328125,
+          "text": "What"
+        },
+        {
+          "id": 374,
+          "logprob": -1.2265625,
+          "text": " is"
+        },
+        {
+          "id": 5655,
+          "logprob": -9.1015625,
+          "text": " deep"
+        },
+        {
+          "id": 6975,
+          "logprob": -1.8085938,
+          "text": " learning"
+        },
+        {
+          "id": 30,
+          "logprob": -1.0439453,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 18682,
+          "logprob": -2.1992188,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 6975,
+          "logprob": -0.07897949,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 374,
+          "logprob": -0.27734375,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 264,
+          "logprob": -0.37402344,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 27084,
+          "logprob": -1.4511719,
+          "special": false,
+          "text": " subset"
+        },
+        {
+          "id": 315,
+          "logprob": -0.02909851,
+          "special": false,
+          "text": " of"
+        },
+        {
+          "id": 5780,
+          "logprob": -0.25854492,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 6975,
+          "logprob": -0.0061798096,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 430,
+          "logprob": -1.3046875,
+          "special": false,
+          "text": " that"
+        },
+        {
+          "id": 374,
+          "logprob": -1.5537109,
+          "special": false,
+          "text": " is"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": " Deep learning is a subset of machine learning that is"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 128000,
+          "logprob": null,
+          "text": "<|begin_of_text|>"
+        },
+        {
+          "id": 3923,
+          "logprob": -5.6328125,
+          "text": "What"
+        },
+        {
+          "id": 374,
+          "logprob": -1.2265625,
+          "text": " is"
+        },
+        {
+          "id": 5655,
+          "logprob": -9.1015625,
+          "text": " deep"
+        },
+        {
+          "id": 6975,
+          "logprob": -1.8085938,
+          "text": " learning"
+        },
+        {
+          "id": 30,
+          "logprob": -1.0439453,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 18682,
+          "logprob": -2.1992188,
+          "special": false,
+          "text": " Deep"
+        },
+        {
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new file mode 100644
index 00000000..9ca22e10
--- /dev/null
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diff --git a/integration-tests/models/__snapshots__/test_flash_mixtral_awq/test_flash_mixtral_awq_all_params.json b/integration-tests/models/__snapshots__/test_flash_mixtral_awq/test_flash_mixtral_awq_all_params.json
new file mode 100644
index 00000000..38ab7263
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_mixtral_awq/test_flash_mixtral_awq_all_params.json
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diff --git a/integration-tests/models/__snapshots__/test_flash_mixtral_awq/test_flash_mixtral_awq_load.json b/integration-tests/models/__snapshots__/test_flash_mixtral_awq/test_flash_mixtral_awq_load.json
new file mode 100644
index 00000000..329d73ee
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_mixtral_awq/test_flash_mixtral_awq_load.json
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diff --git a/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq.json b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq.json
new file mode 100644
index 00000000..993bdadd
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq.json
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diff --git a/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_all_params.json b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_all_params.json
new file mode 100644
index 00000000..94411eef
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_all_params.json
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+        "special": false,
+        "text": "\n"
+      },
+      {
+        "id": 2287,
+        "logprob": 0.0,
+        "special": false,
+        "text": "   "
+      },
+      {
+        "id": 345,
+        "logprob": 0.0,
+        "special": false,
+        "text": " \""
+      },
+      {
+        "id": 3134,
+        "logprob": -0.640625,
+        "special": false,
+        "text": "request"
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": "Test request\n```json\n{\n    \"request"
+}
diff --git a/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_load.json b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_load.json
new file mode 100644
index 00000000..19e306a3
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_load.json
@@ -0,0 +1,358 @@
+[
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1,
+          "logprob": null,
+          "text": "<s>"
+        },
+        {
+          "id": 3735,
+          "logprob": -11.0078125,
+          "text": "Test"
+        },
+        {
+          "id": 2159,
+          "logprob": -13.59375,
+          "text": "request"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.7089844,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.68847656,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 28771,
+          "logprob": -1.9394531,
+          "special": false,
+          "text": "#"
+        },
+        {
+          "id": 3735,
+          "logprob": -2.8828125,
+          "special": false,
+          "text": " Test"
+        },
+        {
+          "id": 2159,
+          "logprob": -0.37329102,
+          "special": false,
+          "text": " request"
+        },
+        {
+          "id": 13,
+          "logprob": -0.2602539,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.0017185211,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 1064,
+          "logprob": -2.2753906,
+          "special": false,
+          "text": "##"
+        },
+        {
+          "id": 3735,
+          "logprob": -1.9316406,
+          "special": false,
+          "text": " Test"
+        },
+        {
+          "id": 2159,
+          "logprob": -0.48217773,
+          "special": false,
+          "text": " request"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\n\n# Test request\n\n## Test request"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
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+          "text": "<s>"
+        },
+        {
+          "id": 3735,
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+        },
+        {
+          "id": 2159,
+          "logprob": -13.59375,
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+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.7089844,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.68847656,
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+          "text": "\n"
+        },
+        {
+          "id": 28771,
+          "logprob": -1.9394531,
+          "special": false,
+          "text": "#"
+        },
+        {
+          "id": 3735,
+          "logprob": -2.8828125,
+          "special": false,
+          "text": " Test"
+        },
+        {
+          "id": 2159,
+          "logprob": -0.37329102,
+          "special": false,
+          "text": " request"
+        },
+        {
+          "id": 13,
+          "logprob": -0.2602539,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.0017185211,
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+          "text": "\n"
+        },
+        {
+          "id": 1064,
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+        },
+        {
+          "id": 3735,
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+        },
+        {
+          "id": 2159,
+          "logprob": -0.48217773,
+          "special": false,
+          "text": " request"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\n\n# Test request\n\n## Test request"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1,
+          "logprob": null,
+          "text": "<s>"
+        },
+        {
+          "id": 3735,
+          "logprob": -11.0078125,
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+        },
+        {
+          "id": 2159,
+          "logprob": -13.59375,
+          "text": "request"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.7089844,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.68847656,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 28771,
+          "logprob": -1.9394531,
+          "special": false,
+          "text": "#"
+        },
+        {
+          "id": 3735,
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+          "special": false,
+          "text": " Test"
+        },
+        {
+          "id": 2159,
+          "logprob": -0.37329102,
+          "special": false,
+          "text": " request"
+        },
+        {
+          "id": 13,
+          "logprob": -0.2602539,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.0017185211,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 1064,
+          "logprob": -2.2753906,
+          "special": false,
+          "text": "##"
+        },
+        {
+          "id": 3735,
+          "logprob": -1.9316406,
+          "special": false,
+          "text": " Test"
+        },
+        {
+          "id": 2159,
+          "logprob": -0.48217773,
+          "special": false,
+          "text": " request"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\n\n# Test request\n\n## Test request"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1,
+          "logprob": null,
+          "text": "<s>"
+        },
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+          "logprob": -11.0078125,
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+        },
+        {
+          "id": 2159,
+          "logprob": -13.59375,
+          "text": "request"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.7089844,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.68847656,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 28771,
+          "logprob": -1.9394531,
+          "special": false,
+          "text": "#"
+        },
+        {
+          "id": 3735,
+          "logprob": -2.8828125,
+          "special": false,
+          "text": " Test"
+        },
+        {
+          "id": 2159,
+          "logprob": -0.37329102,
+          "special": false,
+          "text": " request"
+        },
+        {
+          "id": 13,
+          "logprob": -0.2602539,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.0017185211,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 1064,
+          "logprob": -2.2753906,
+          "special": false,
+          "text": "##"
+        },
+        {
+          "id": 3735,
+          "logprob": -1.9316406,
+          "special": false,
+          "text": " Test"
+        },
+        {
+          "id": 2159,
+          "logprob": -0.48217773,
+          "special": false,
+          "text": " request"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\n\n# Test request\n\n## Test request"
+  }
+]
diff --git a/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe.json b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe.json
new file mode 100644
index 00000000..0d6dca31
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe.json
@@ -0,0 +1,109 @@
+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 10,
+    "prefill": [
+      {
+        "id": 1724,
+        "logprob": null,
+        "text": "What"
+      },
+      {
+        "id": 338,
+        "logprob": -0.7133789,
+        "text": "is"
+      },
+      {
+        "id": 16030,
+        "logprob": -13.9296875,
+        "text": "gradient"
+      },
+      {
+        "id": 26815,
+        "logprob": -0.048919678,
+        "text": "descent"
+      },
+      {
+        "id": 29973,
+        "logprob": -3.0078125,
+        "text": "?"
+      },
+      {
+        "id": 13,
+        "logprob": -2.8105469,
+        "text": "\n"
+      },
+      {
+        "id": 13,
+        "logprob": -0.84521484,
+        "text": "\n"
+      }
+    ],
+    "seed": null,
+    "tokens": [
+      {
+        "id": 25584,
+        "logprob": -0.017028809,
+        "special": false,
+        "text": "Grad"
+      },
+      {
+        "id": 993,
+        "logprob": -0.0027313232,
+        "special": false,
+        "text": "ient"
+      },
+      {
+        "id": 26815,
+        "logprob": -0.023254395,
+        "special": false,
+        "text": " descent"
+      },
+      {
+        "id": 338,
+        "logprob": -2.0623207e-05,
+        "special": false,
+        "text": " is"
+      },
+      {
+        "id": 263,
+        "logprob": -0.5361328,
+        "special": false,
+        "text": " a"
+      },
+      {
+        "id": 937,
+        "logprob": -0.17578125,
+        "special": false,
+        "text": " first"
+      },
+      {
+        "id": 29899,
+        "logprob": 0.0,
+        "special": false,
+        "text": "-"
+      },
+      {
+        "id": 2098,
+        "logprob": -0.00011539459,
+        "special": false,
+        "text": "order"
+      },
+      {
+        "id": 13883,
+        "logprob": -0.47436523,
+        "special": false,
+        "text": " optimization"
+      },
+      {
+        "id": 5687,
+        "logprob": -0.00027680397,
+        "special": false,
+        "text": " algorithm"
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": "Gradient descent is a first-order optimization algorithm"
+}
diff --git a/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json
new file mode 100644
index 00000000..38b80335
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json
@@ -0,0 +1,99 @@
+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 10,
+    "prefill": [
+      {
+        "id": 16030,
+        "logprob": null,
+        "text": "gradient"
+      },
+      {
+        "id": 26815,
+        "logprob": -6.4960938,
+        "text": "descent"
+      },
+      {
+        "id": 29973,
+        "logprob": -5.1484375,
+        "text": "?"
+      },
+      {
+        "id": 13,
+        "logprob": -4.0351562,
+        "text": "\n"
+      },
+      {
+        "id": 13,
+        "logprob": -5.2265625,
+        "text": "\n"
+      }
+    ],
+    "seed": 0,
+    "tokens": [
+      {
+        "id": 10994,
+        "logprob": -1.1542969,
+        "special": false,
+        "text": "Hello"
+      },
+      {
+        "id": 29991,
+        "logprob": 0.0,
+        "special": false,
+        "text": "!"
+      },
+      {
+        "id": 739,
+        "logprob": 0.0,
+        "special": false,
+        "text": " It"
+      },
+      {
+        "id": 2444,
+        "logprob": -0.42260742,
+        "special": false,
+        "text": " seems"
+      },
+      {
+        "id": 366,
+        "logprob": 0.0,
+        "special": false,
+        "text": " you"
+      },
+      {
+        "id": 29915,
+        "logprob": 0.0,
+        "special": false,
+        "text": "'"
+      },
+      {
+        "id": 276,
+        "logprob": -0.9838867,
+        "special": false,
+        "text": "re"
+      },
+      {
+        "id": 3211,
+        "logprob": 0.0,
+        "special": false,
+        "text": " address"
+      },
+      {
+        "id": 292,
+        "logprob": 0.0,
+        "special": false,
+        "text": "ing"
+      },
+      {
+        "id": 263,
+        "logprob": -0.15124512,
+        "special": false,
+        "text": " a"
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": "What is gradient descent?\n\nHello! It seems you're addressing a"
+}
diff --git a/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_load.json b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_load.json
new file mode 100644
index 00000000..f1f81152
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_load.json
@@ -0,0 +1,438 @@
+[
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1724,
+          "logprob": null,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -0.7133789,
+          "text": "is"
+        },
+        {
+          "id": 16030,
+          "logprob": -13.9296875,
+          "text": "gradient"
+        },
+        {
+          "id": 26815,
+          "logprob": -0.048919678,
+          "text": "descent"
+        },
+        {
+          "id": 29973,
+          "logprob": -3.0078125,
+          "text": "?"
+        },
+        {
+          "id": 13,
+          "logprob": -2.8105469,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.84521484,
+          "text": "\n"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 25584,
+          "logprob": -0.017028809,
+          "special": false,
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+        },
+        {
+          "id": 993,
+          "logprob": -0.0028476715,
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+        },
+        {
+          "id": 26815,
+          "logprob": -0.023971558,
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+        },
+        {
+          "id": 338,
+          "logprob": -2.0384789e-05,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 263,
+          "logprob": -0.5229492,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 937,
+          "logprob": -0.17602539,
+          "special": false,
+          "text": " first"
+        },
+        {
+          "id": 29899,
+          "logprob": 0.0,
+          "special": false,
+          "text": "-"
+        },
+        {
+          "id": 2098,
+          "logprob": -0.000116467476,
+          "special": false,
+          "text": "order"
+        },
+        {
+          "id": 13883,
+          "logprob": -0.47436523,
+          "special": false,
+          "text": " optimization"
+        },
+        {
+          "id": 5687,
+          "logprob": -0.00027871132,
+          "special": false,
+          "text": " algorithm"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "Gradient descent is a first-order optimization algorithm"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
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+          "logprob": null,
+          "text": "What"
+        },
+        {
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+          "logprob": -0.7128906,
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+          "logprob": -13.9375,
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+        {
+          "id": 26815,
+          "logprob": -0.05053711,
+          "text": "descent"
+        },
+        {
+          "id": 29973,
+          "logprob": -3.0058594,
+          "text": "?"
+        },
+        {
+          "id": 13,
+          "logprob": -2.8242188,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.84521484,
+          "text": "\n"
+        }
+      ],
+      "seed": null,
+      "tokens": [
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+          "logprob": -0.018859863,
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+          "id": 993,
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+        },
+        {
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+        },
+        {
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+        {
+          "id": 5687,
+          "logprob": -0.00027036667,
+          "special": false,
+          "text": " algorithm"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "Gradient descent is a first-order optimization algorithm"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
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+          "id": 1724,
+          "logprob": null,
+          "text": "What"
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+        {
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+          "logprob": -0.71484375,
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+        },
+        {
+          "id": 29973,
+          "logprob": -3.0078125,
+          "text": "?"
+        },
+        {
+          "id": 13,
+          "logprob": -2.8242188,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.86328125,
+          "text": "\n"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 25584,
+          "logprob": -0.017196655,
+          "special": false,
+          "text": "Grad"
+        },
+        {
+          "id": 993,
+          "logprob": -0.0028438568,
+          "special": false,
+          "text": "ient"
+        },
+        {
+          "id": 26815,
+          "logprob": -0.023254395,
+          "special": false,
+          "text": " descent"
+        },
+        {
+          "id": 338,
+          "logprob": -2.026558e-05,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 263,
+          "logprob": -0.5229492,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 937,
+          "logprob": -0.17602539,
+          "special": false,
+          "text": " first"
+        },
+        {
+          "id": 29899,
+          "logprob": 0.0,
+          "special": false,
+          "text": "-"
+        },
+        {
+          "id": 2098,
+          "logprob": -0.00011622906,
+          "special": false,
+          "text": "order"
+        },
+        {
+          "id": 13883,
+          "logprob": -0.48608398,
+          "special": false,
+          "text": " optimization"
+        },
+        {
+          "id": 5687,
+          "logprob": -0.00027894974,
+          "special": false,
+          "text": " algorithm"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "Gradient descent is a first-order optimization algorithm"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1724,
+          "logprob": null,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -0.7192383,
+          "text": "is"
+        },
+        {
+          "id": 16030,
+          "logprob": -13.9375,
+          "text": "gradient"
+        },
+        {
+          "id": 26815,
+          "logprob": -0.050445557,
+          "text": "descent"
+        },
+        {
+          "id": 29973,
+          "logprob": -3.0078125,
+          "text": "?"
+        },
+        {
+          "id": 13,
+          "logprob": -2.8242188,
+          "text": "\n"
+        },
+        {
+          "id": 13,
+          "logprob": -0.8276367,
+          "text": "\n"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 25584,
+          "logprob": -0.01727295,
+          "special": false,
+          "text": "Grad"
+        },
+        {
+          "id": 993,
+          "logprob": -0.0027542114,
+          "special": false,
+          "text": "ient"
+        },
+        {
+          "id": 26815,
+          "logprob": -0.023254395,
+          "special": false,
+          "text": " descent"
+        },
+        {
+          "id": 338,
+          "logprob": -2.0384789e-05,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 263,
+          "logprob": -0.5229492,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 937,
+          "logprob": -0.17126465,
+          "special": false,
+          "text": " first"
+        },
+        {
+          "id": 29899,
+          "logprob": 0.0,
+          "special": false,
+          "text": "-"
+        },
+        {
+          "id": 2098,
+          "logprob": -0.00011301041,
+          "special": false,
+          "text": "order"
+        },
+        {
+          "id": 13883,
+          "logprob": -0.48608398,
+          "special": false,
+          "text": " optimization"
+        },
+        {
+          "id": 5687,
+          "logprob": -0.00027894974,
+          "special": false,
+          "text": " algorithm"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "Gradient descent is a first-order optimization algorithm"
+  }
+]
diff --git a/integration-tests/models/__snapshots__/test_mllama/test_mllama_load.json b/integration-tests/models/__snapshots__/test_mllama/test_mllama_load.json
new file mode 100644
index 00000000..95ba7a78
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_mllama/test_mllama_load.json
@@ -0,0 +1,106 @@
+[
+  {
+    "choices": [
+      {
+        "finish_reason": "length",
+        "index": 0,
+        "logprobs": null,
+        "message": {
+          "content": "In a bustling city, a chicken named Cluck",
+          "name": null,
+          "role": "assistant",
+          "tool_calls": null
+        },
+        "usage": null
+      }
+    ],
+    "created": 1727773835,
+    "id": "",
+    "model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
+    "object": "chat.completion",
+    "system_fingerprint": "2.3.1-dev0-native",
+    "usage": {
+      "completion_tokens": 10,
+      "prompt_tokens": 50,
+      "total_tokens": 60
+    }
+  },
+  {
+    "choices": [
+      {
+        "finish_reason": "length",
+        "index": 0,
+        "logprobs": null,
+        "message": {
+          "content": "In a world where even chickens could dream big,",
+          "name": null,
+          "role": "assistant",
+          "tool_calls": null
+        },
+        "usage": null
+      }
+    ],
+    "created": 1727773835,
+    "id": "",
+    "model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
+    "object": "chat.completion",
+    "system_fingerprint": "2.3.1-dev0-native",
+    "usage": {
+      "completion_tokens": 10,
+      "prompt_tokens": 50,
+      "total_tokens": 60
+    }
+  },
+  {
+    "choices": [
+      {
+        "finish_reason": "length",
+        "index": 0,
+        "logprobs": null,
+        "message": {
+          "content": "In a world where even chickens could dream big,",
+          "name": null,
+          "role": "assistant",
+          "tool_calls": null
+        },
+        "usage": null
+      }
+    ],
+    "created": 1727773835,
+    "id": "",
+    "model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
+    "object": "chat.completion",
+    "system_fingerprint": "2.3.1-dev0-native",
+    "usage": {
+      "completion_tokens": 10,
+      "prompt_tokens": 50,
+      "total_tokens": 60
+    }
+  },
+  {
+    "choices": [
+      {
+        "finish_reason": "length",
+        "index": 0,
+        "logprobs": null,
+        "message": {
+          "content": "In a world where even chickens could dream big,",
+          "name": null,
+          "role": "assistant",
+          "tool_calls": null
+        },
+        "usage": null
+      }
+    ],
+    "created": 1727773835,
+    "id": "",
+    "model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
+    "object": "chat.completion",
+    "system_fingerprint": "2.3.1-dev0-native",
+    "usage": {
+      "completion_tokens": 10,
+      "prompt_tokens": 50,
+      "total_tokens": 60
+    }
+  }
+]
diff --git a/integration-tests/models/__snapshots__/test_mllama/test_mllama_simpl.json b/integration-tests/models/__snapshots__/test_mllama/test_mllama_simpl.json
new file mode 100644
index 00000000..1d4dd6d7
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_mllama/test_mllama_simpl.json
@@ -0,0 +1,26 @@
+{
+  "choices": [
+    {
+      "finish_reason": "length",
+      "index": 0,
+      "logprobs": null,
+      "message": {
+        "content": "In a bustling city, a chicken named Cluck",
+        "name": null,
+        "role": "assistant",
+        "tool_calls": null
+      },
+      "usage": null
+    }
+  ],
+  "created": 1727556016,
+  "id": "",
+  "model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
+  "object": "chat.completion",
+  "system_fingerprint": "2.3.1-dev0-native",
+  "usage": {
+    "completion_tokens": 10,
+    "prompt_tokens": 50,
+    "total_tokens": 60
+  }
+}
diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information.json
index 0cd3c67f..70b20362 100644
--- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information.json
+++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information.json
@@ -1,38 +1,26 @@
 {
   "choices": [
     {
-      "finish_reason": "eos_token",
+      "finish_reason": "stop",
       "index": 0,
       "logprobs": null,
       "message": {
-        "content": null,
+        "content": "I am an AI assistant",
         "name": null,
         "role": "assistant",
-        "tool_calls": [
-          {
-            "function": {
-              "arguments": {
-                "error": "Cannot get current weather forecast from specified location and temperature unit. Please try again with different options."
-              },
-              "description": null,
-              "name": "notify_error"
-            },
-            "id": 0,
-            "type": "function"
-          }
-        ]
+        "tool_calls": null
       },
       "usage": null
     }
   ],
-  "created": 1712852597,
+  "created": 1728497062,
   "id": "",
-  "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
-  "object": "text_completion",
-  "system_fingerprint": "1.4.5-native",
+  "model": "meta-llama/Llama-3.1-8B-Instruct",
+  "object": "chat.completion",
+  "system_fingerprint": "2.3.2-dev0-native",
   "usage": {
-    "completion_tokens": 39,
-    "prompt_tokens": 496,
-    "total_tokens": 535
+    "completion_tokens": 23,
+    "prompt_tokens": 604,
+    "total_tokens": 627
   }
 }
diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information_stream.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information_stream.json
new file mode 100644
index 00000000..fa208c54
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information_stream.json
@@ -0,0 +1,20 @@
+{
+  "choices": [
+    {
+      "delta": {
+        "content": " assistant",
+        "role": "assistant",
+        "tool_calls": null
+      },
+      "finish_reason": null,
+      "index": 0,
+      "logprobs": null
+    }
+  ],
+  "created": 1728497531,
+  "id": "",
+  "model": "meta-llama/Llama-3.1-8B-Instruct",
+  "object": "chat.completion.chunk",
+  "system_fingerprint": "2.3.2-dev0-native",
+  "usage": null
+}
diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_sea_creatures_stream.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_sea_creatures_stream.json
new file mode 100644
index 00000000..72232e17
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_sea_creatures_stream.json
@@ -0,0 +1,20 @@
+{
+  "choices": [
+    {
+      "delta": {
+        "content": " fans",
+        "role": "assistant",
+        "tool_calls": null
+      },
+      "finish_reason": null,
+      "index": 0,
+      "logprobs": null
+    }
+  ],
+  "created": 1728497461,
+  "id": "",
+  "model": "meta-llama/Llama-3.1-8B-Instruct",
+  "object": "chat.completion.chunk",
+  "system_fingerprint": "2.3.2-dev0-native",
+  "usage": null
+}
diff --git a/integration-tests/models/test_flash_gemma.py b/integration-tests/models/test_flash_gemma.py
index 7bee8dea..4bd7bd14 100644
--- a/integration-tests/models/test_flash_gemma.py
+++ b/integration-tests/models/test_flash_gemma.py
@@ -16,7 +16,7 @@ async def flash_gemma(flash_gemma_handle):
 @pytest.mark.release
 @pytest.mark.asyncio
 @pytest.mark.private
-async def test_flash_gemma(flash_gemma, response_snapshot):
+async def test_flash_gemma_simple(flash_gemma, response_snapshot):
     response = await flash_gemma.generate(
         "Test request", max_new_tokens=10, decoder_input_details=True
     )
diff --git a/integration-tests/models/test_flash_llama.py b/integration-tests/models/test_flash_llama.py
index c69314ff..bf49dc0b 100644
--- a/integration-tests/models/test_flash_llama.py
+++ b/integration-tests/models/test_flash_llama.py
@@ -15,7 +15,7 @@ async def flash_llama(flash_llama_handle):
 
 @pytest.mark.asyncio
 @pytest.mark.private
-async def test_flash_llama(flash_llama, response_snapshot):
+async def test_flash_llama_simple(flash_llama, response_snapshot):
     response = await flash_llama.generate(
         "Test request", max_new_tokens=10, decoder_input_details=True
     )
diff --git a/integration-tests/models/test_flash_llama_fp8_kv_cache.py b/integration-tests/models/test_flash_llama_fp8_kv_cache.py
new file mode 100644
index 00000000..05e9f0dd
--- /dev/null
+++ b/integration-tests/models/test_flash_llama_fp8_kv_cache.py
@@ -0,0 +1,77 @@
+import pytest
+
+
+@pytest.fixture(scope="module")
+def flash_llama_fp8_kv_cache_handle(launcher):
+    with launcher(
+        "meta-llama/Meta-Llama-3-8B", num_shard=2, kv_cache_dtype="fp8_e5m2"
+    ) as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def flash_llama_fp8_kv_cache(flash_llama_fp8_kv_cache_handle):
+    await flash_llama_fp8_kv_cache_handle.health(300)
+    return flash_llama_fp8_kv_cache_handle.client
+
+
+@pytest.mark.release
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_llama_fp8_kv_cache(flash_llama_fp8_kv_cache, response_snapshot):
+    response = await flash_llama_fp8_kv_cache.generate(
+        "What is deep learning?", max_new_tokens=10, decoder_input_details=True
+    )
+
+    assert (
+        response.generated_text
+        == " Deep learning is a subset of machine learning that is"
+    )
+    assert response.details.generated_tokens == 10
+    assert response == response_snapshot
+
+
+@pytest.mark.release
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_llama_fp8_kv_cache_all_params(
+    flash_llama_fp8_kv_cache, response_snapshot
+):
+    response = await flash_llama_fp8_kv_cache.generate(
+        "What is deep learning?",
+        max_new_tokens=10,
+        repetition_penalty=1.2,
+        return_full_text=True,
+        stop_sequences=["test"],
+        temperature=0.5,
+        top_p=0.9,
+        top_k=10,
+        truncate=5,
+        typical_p=0.9,
+        watermark=True,
+        decoder_input_details=True,
+        seed=0,
+    )
+
+    assert response == response_snapshot
+
+
+@pytest.mark.release
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_llama_fp8_kv_cache_load(
+    flash_llama_fp8_kv_cache, generate_load, response_snapshot
+):
+    responses = await generate_load(
+        flash_llama_fp8_kv_cache, "What is deep learning?", max_new_tokens=10, n=4
+    )
+
+    assert len(responses) == 4
+    assert (
+        responses[0].generated_text
+        == " Deep learning is a subset of machine learning that is"
+    )
+    assert all(
+        [r.generated_text == responses[0].generated_text for r in responses]
+    ), f"Different messages : {[r.generated_text for r in responses]}"
+    assert responses == response_snapshot
diff --git a/integration-tests/models/test_flash_mixtral_awq.py b/integration-tests/models/test_flash_mixtral_awq.py
new file mode 100644
index 00000000..ab1e0f00
--- /dev/null
+++ b/integration-tests/models/test_flash_mixtral_awq.py
@@ -0,0 +1,73 @@
+import pytest
+
+
+@pytest.fixture(scope="module")
+def flash_mixtral_awq_handle(launcher):
+    with launcher("casperhansen/mixtral-instruct-awq", num_shard=2) as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def flash_mixtral_awq(flash_mixtral_awq_handle):
+    await flash_mixtral_awq_handle.health(300)
+    return flash_mixtral_awq_handle.client
+
+
+@pytest.mark.asyncio
+async def test_flash_mixtral_awq(flash_mixtral_awq, response_snapshot):
+    response = await flash_mixtral_awq.generate(
+        "What is deep learning?", max_new_tokens=10, decoder_input_details=True
+    )
+
+    assert response.details.generated_tokens == 10
+    assert (
+        response.generated_text == "\n\nDeep learning is a subset of machine learning"
+    )
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+async def test_flash_mixtral_awq_all_params(flash_mixtral_awq, response_snapshot):
+    response = await flash_mixtral_awq.generate(
+        "What is deep learning?",
+        max_new_tokens=10,
+        repetition_penalty=1.2,
+        return_full_text=True,
+        stop_sequences=["test"],
+        temperature=0.5,
+        top_p=0.9,
+        top_k=10,
+        truncate=5,
+        typical_p=0.9,
+        watermark=True,
+        decoder_input_details=True,
+        seed=0,
+    )
+
+    assert response.details.generated_tokens == 10
+    assert (
+        response.generated_text
+        == "What is deep learning?\nDeep Learning is a subset of Machine Learning,"
+    )
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+async def test_flash_mixtral_awq_load(
+    flash_mixtral_awq, generate_load, response_snapshot
+):
+    responses = await generate_load(
+        flash_mixtral_awq, "What is deep learning?", max_new_tokens=10, n=4
+    )
+
+    assert len(responses) == 4
+    assert responses[0].details.generated_tokens == 10
+    assert (
+        responses[0].generated_text
+        == "\n\nDeep learning is a subset of machine learning"
+    )
+    assert all(
+        [r.generated_text == responses[0].generated_text for r in responses]
+    ), f"{[r.generated_text  for r in responses]}"
+
+    assert responses == response_snapshot
diff --git a/integration-tests/models/test_flash_mixtral_gptq.py b/integration-tests/models/test_flash_mixtral_gptq.py
new file mode 100644
index 00000000..eb880628
--- /dev/null
+++ b/integration-tests/models/test_flash_mixtral_gptq.py
@@ -0,0 +1,60 @@
+import pytest
+
+
+@pytest.fixture(scope="module")
+def flash_mixtral_gptq_handle(launcher):
+    with launcher("TheBloke/Mixtral-8x7B-Instruct-v0.1-GPTQ", num_shard=2) as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def flash_mixtral_gptq(flash_mixtral_gptq_handle):
+    await flash_mixtral_gptq_handle.health(300)
+    return flash_mixtral_gptq_handle.client
+
+
+@pytest.mark.asyncio
+async def test_flash_mixtral_gptq(flash_mixtral_gptq, response_snapshot):
+    response = await flash_mixtral_gptq.generate(
+        "Test request", max_new_tokens=10, decoder_input_details=True
+    )
+
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+async def test_flash_mixtral_gptq_all_params(flash_mixtral_gptq, response_snapshot):
+    response = await flash_mixtral_gptq.generate(
+        "Test request",
+        max_new_tokens=10,
+        repetition_penalty=1.2,
+        return_full_text=True,
+        stop_sequences=["test"],
+        temperature=0.5,
+        top_p=0.9,
+        top_k=10,
+        truncate=5,
+        typical_p=0.9,
+        watermark=True,
+        decoder_input_details=True,
+        seed=0,
+    )
+
+    assert response.details.generated_tokens == 10
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+async def test_flash_mixtral_gptq_load(
+    flash_mixtral_gptq, generate_load, response_snapshot
+):
+    responses = await generate_load(
+        flash_mixtral_gptq, "Test request", max_new_tokens=10, n=4
+    )
+
+    assert len(responses) == 4
+    assert all(
+        [r.generated_text == responses[0].generated_text for r in responses]
+    ), f"{[r.generated_text  for r in responses]}"
+
+    assert responses == response_snapshot
diff --git a/integration-tests/models/test_flash_phi35_moe.py b/integration-tests/models/test_flash_phi35_moe.py
new file mode 100644
index 00000000..2173740a
--- /dev/null
+++ b/integration-tests/models/test_flash_phi35_moe.py
@@ -0,0 +1,75 @@
+import pytest
+
+
+@pytest.fixture(scope="module")
+def flash_phi35_moe_handle(launcher):
+    with launcher(
+        "microsoft/Phi-3.5-MoE-instruct",
+        num_shard=4,
+    ) as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def flash_phi35_moe(flash_phi35_moe_handle):
+    await flash_phi35_moe_handle.health(300)
+    return flash_phi35_moe_handle.client
+
+
+@pytest.mark.asyncio
+async def test_flash_phi35_moe(flash_phi35_moe, response_snapshot):
+    response = await flash_phi35_moe.generate(
+        "What is gradient descent?\n\n", max_new_tokens=10, decoder_input_details=True
+    )
+
+    assert response.details.generated_tokens == 10
+    assert (
+        response.generated_text
+        == "Gradient descent is a first-order optimization algorithm"
+    )
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+async def test_flash_phi35_moe_all_params(flash_phi35_moe, response_snapshot):
+    response = await flash_phi35_moe.generate(
+        "What is gradient descent?\n\n",
+        max_new_tokens=10,
+        repetition_penalty=1.2,
+        return_full_text=True,
+        stop_sequences=["test"],
+        temperature=0.5,
+        top_p=0.9,
+        top_k=10,
+        truncate=5,
+        typical_p=0.9,
+        watermark=True,
+        decoder_input_details=True,
+        seed=0,
+    )
+
+    assert response.details.generated_tokens == 10
+    assert (
+        response.generated_text
+        == "What is gradient descent?\n\nHello! It seems you're addressing a"
+    )
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+async def test_flash_phi35_moe_load(flash_phi35_moe, generate_load, response_snapshot):
+    responses = await generate_load(
+        flash_phi35_moe, "What is gradient descent?\n\n", max_new_tokens=10, n=4
+    )
+
+    assert len(responses) == 4
+    assert responses[0].details.generated_tokens == 10
+    assert (
+        responses[0].generated_text
+        == "Gradient descent is a first-order optimization algorithm"
+    )
+    assert all(
+        [r.generated_text == responses[0].generated_text for r in responses]
+    ), f"{[r.generated_text  for r in responses]}"
+
+    assert responses == response_snapshot
diff --git a/integration-tests/models/test_mllama.py b/integration-tests/models/test_mllama.py
new file mode 100644
index 00000000..1b4264aa
--- /dev/null
+++ b/integration-tests/models/test_mllama.py
@@ -0,0 +1,105 @@
+import pytest
+import base64
+import asyncio
+
+
+@pytest.fixture(scope="module")
+def mllama_handle(launcher):
+    with launcher("meta-llama/Llama-3.2-11B-Vision-Instruct", num_shard=2) as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def mllama(mllama_handle):
+    await mllama_handle.health(300)
+    return mllama_handle.client
+
+
+# TODO fix the server parsser to count inline image tokens correctly
+def get_chicken():
+    with open("integration-tests/images/chicken_on_money.png", "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read())
+    return f"data:image/png;base64,{encoded_string.decode('utf-8')}"
+
+
+def get_cow_beach():
+    with open("integration-tests/images/cow_beach.png", "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read())
+    return f"data:image/png;base64,{encoded_string.decode('utf-8')}"
+
+
+@pytest.mark.asyncio
+async def test_mllama_simpl(mllama, response_snapshot):
+    # chicken = get_chicken()
+    response = await mllama.chat(
+        max_tokens=10,
+        temperature=0.0,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": "Can you tell me a very short story based on the image?",
+                    },
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": "https://raw.githubusercontent.com/huggingface/text-generation-inference/main/integration-tests/images/chicken_on_money.png"
+                        },
+                    },
+                ],
+            },
+        ],
+    )
+
+    assert response.usage == {
+        "completion_tokens": 10,
+        "prompt_tokens": 50,
+        "total_tokens": 60,
+    }
+    assert (
+        response.choices[0].message.content
+        == "In a bustling city, a chicken named Cluck"
+    )
+    assert response == response_snapshot
+
+
+@pytest.mark.release
+@pytest.mark.asyncio
+async def test_mllama_load(mllama, generate_load, response_snapshot):
+    futures = [
+        mllama.chat(
+            max_tokens=10,
+            temperature=0.0,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": "Can you tell me a very short story based on the image?",
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": "https://raw.githubusercontent.com/huggingface/text-generation-inference/main/integration-tests/images/chicken_on_money.png"
+                            },
+                        },
+                    ],
+                },
+            ],
+        )
+        for i in range(4)
+    ]
+    responses = await asyncio.gather(*futures)
+
+    generated_texts = [response.choices[0].message.content for response in responses]
+
+    assert generated_texts[0] == "In a bustling city, a chicken named Cluck"
+    assert len(generated_texts) == 4
+    assert generated_texts, all(
+        [text == generated_texts[0] for text in generated_texts]
+    )
+
+    assert responses == response_snapshot
diff --git a/integration-tests/models/test_tools_llama.py b/integration-tests/models/test_tools_llama.py
index 9855cfda..98e75bb4 100644
--- a/integration-tests/models/test_tools_llama.py
+++ b/integration-tests/models/test_tools_llama.py
@@ -4,7 +4,9 @@ import pytest
 @pytest.fixture(scope="module")
 def flash_llama_grammar_tools_handle(launcher):
     with launcher(
-        "TinyLlama/TinyLlama-1.1B-Chat-v1.0", num_shard=2, disable_grammar_support=False
+        "meta-llama/Meta-Llama-3.1-8B-Instruct",
+        num_shard=2,
+        disable_grammar_support=False,
     ) as handle:
         yield handle
 
@@ -205,11 +207,20 @@ async def test_flash_llama_grammar_tools_stream(
     )
 
     count = 0
+    tool_calls_generated = ""
+    last_response = None
     async for response in responses:
         count += 1
+        tool_calls_generated += response.choices[0].delta.tool_calls.function.arguments
+        last_response = response
+        assert response.choices[0].delta.content is None
 
-    assert count == 48
-    assert response == response_snapshot
+    assert (
+        tool_calls_generated
+        == '{"function": {"_name": "get_current_weather", "format": "celsius", "location": "Paris, France"}}<|eot_id|>'
+    )
+    assert count == 28
+    assert last_response == response_snapshot
 
 
 @pytest.mark.asyncio
@@ -225,18 +236,94 @@ async def test_flash_llama_grammar_tools_insufficient_information(
         messages=[
             {
                 "role": "system",
-                "content": "STRICTLY ONLY RESPOND IF THE USER ASKS A WEATHER RELATED QUESTION",
+                "content": "You're a helpful assistant! Answer the users question best you can.",
+            },
+            {
+                "role": "user",
+                "content": "Who are you?",
+            },
+        ],
+        stream=False,
+    )
+
+    assert responses.choices[0].message.tool_calls is None
+    assert responses.choices[0].message.content == "I am an AI assistant"
+
+    assert responses == response_snapshot
+
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_llama_grammar_tools_insufficient_information_stream(
+    flash_llama_grammar_tools, response_snapshot
+):
+    responses = await flash_llama_grammar_tools.chat(
+        max_tokens=100,
+        seed=24,
+        tools=tools,
+        tool_choice="auto",
+        messages=[
+            {
+                "role": "system",
+                "content": "You're a helpful assistant! Answer the users question best you can.",
+            },
+            {
+                "role": "user",
+                "content": "Who are you?",
+            },
+        ],
+        stream=True,
+    )
+
+    count = 0
+    content_generated = ""
+    last_response = None
+    async for response in responses:
+        count += 1
+        content_generated += response.choices[0].delta.content
+        last_response = response
+        assert response.choices[0].delta.tool_calls is None
+
+    assert count == 5
+    assert content_generated == "I am an AI assistant"
+    assert last_response == response_snapshot
+
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_llama_grammar_tools_sea_creatures_stream(
+    flash_llama_grammar_tools, response_snapshot
+):
+    responses = await flash_llama_grammar_tools.chat(
+        max_tokens=100,
+        seed=24,
+        tools=tools,
+        tool_choice="auto",
+        messages=[
+            {
+                "role": "system",
+                "content": "You're a helpful assistant! Answer the users question best you can. If the question is not answerable by the tools, just generate a response.",
             },
             {
                 "role": "user",
                 "content": "Tell me a story about 3 sea creatures",
             },
         ],
-        stream=False,
+        stream=True,
     )
 
-    assert responses.choices[0].message.content is None
+    count = 0
+    content_generated = ""
+    last_response = None
+    async for response in responses:
+        count += 1
+        content_generated += response.choices[0].delta.content
+        last_response = response
+        assert response.choices[0].delta.tool_calls is None
+
+    assert count == 62
     assert (
-        responses.choices[0].message.tool_calls[0]["function"]["name"] == "notify_error"
+        content_generated
+        == "Once upon a time, in the ocean, there lived three sea creatures. There was a wise old octopus named Bob, a mischievous seagull named Sam, and a gentle sea turtle named Luna. They all lived together in a beautiful coral reef, surrounded by colorful fish and swaying sea fans"
     )
-    assert responses == response_snapshot
+    assert last_response == response_snapshot
diff --git a/integration-tests/pyproject.toml b/integration-tests/pyproject.toml
index afd57ea7..e49b98fc 100644
--- a/integration-tests/pyproject.toml
+++ b/integration-tests/pyproject.toml
@@ -13,3 +13,6 @@ pytest = "^7.4.0"
 pytest-asyncio = "^0.21.1"
 docker = "^7"
 numpy = "^1.20"
+
+[tool.isort]
+profile = "black"
diff --git a/launcher/Cargo.toml b/launcher/Cargo.toml
index 033a9a04..fdc3c02c 100644
--- a/launcher/Cargo.toml
+++ b/launcher/Cargo.toml
@@ -18,6 +18,7 @@ serde_json = "1.0.107"
 thiserror = "1.0.59"
 tracing = "0.1.37"
 tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] }
+regex = "1.11.0"
 
 [dev-dependencies]
 float_eq = "1.0.1"
diff --git a/launcher/src/gpu.rs b/launcher/src/gpu.rs
index 755d246a..b565220e 100644
--- a/launcher/src/gpu.rs
+++ b/launcher/src/gpu.rs
@@ -1,9 +1,4 @@
-use std::sync::LazyLock;
-
-pub static COMPUTE_CAPABILITY: LazyLock<Option<(usize, usize)>> =
-    LazyLock::new(get_cuda_capability);
-
-fn get_cuda_capability() -> Option<(usize, usize)> {
+pub fn get_cuda_capability() -> Option<(usize, usize)> {
     use pyo3::prelude::*;
 
     let py_get_capability = |py: Python| -> PyResult<(isize, isize)> {
diff --git a/launcher/src/main.rs b/launcher/src/main.rs
index b9da1714..66a877da 100644
--- a/launcher/src/main.rs
+++ b/launcher/src/main.rs
@@ -5,6 +5,7 @@ use hf_hub::{
 };
 use nix::sys::signal::{self, Signal};
 use nix::unistd::Pid;
+use regex::Regex;
 use serde::Deserialize;
 use std::env;
 use std::ffi::OsString;
@@ -66,7 +67,7 @@ fn get_config(
 }
 
 fn resolve_attention(config: &Option<Config>, lora_adapters: &Option<String>) -> (String, String) {
-    let compute_capability = *gpu::COMPUTE_CAPABILITY;
+    let compute_capability = gpu::get_cuda_capability();
     let mut prefix_caching: Option<String> = std::env::var("USE_PREFIX_CACHING").ok();
     let mut attention: Option<String> = std::env::var("ATTENTION").ok();
     if let Some(config) = config {
@@ -300,6 +301,22 @@ impl std::fmt::Display for Dtype {
     }
 }
 
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum KVCacheDtype {
+    #[clap(name = "fp8_e5m2")]
+    Fp8e5m2,
+}
+
+impl std::fmt::Display for KVCacheDtype {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        match self {
+            KVCacheDtype::Fp8e5m2 => {
+                write!(f, "fp8_e5m2")
+            }
+        }
+    }
+}
+
 #[derive(Clone, Copy, Debug, ValueEnum)]
 enum RopeScaling {
     Linear,
@@ -401,6 +418,12 @@ struct Args {
     #[clap(long, env, value_enum)]
     dtype: Option<Dtype>,
 
+    /// Specify the dtype for the key-value cache. When this option is not provided,
+    /// the dtype of the model is used (typically `float16` or `bfloat16`). Currently
+    /// the only supported value is `fp8_e5m2` on CUDA.
+    #[clap(long, env, value_enum)]
+    kv_cache_dtype: Option<KVCacheDtype>,
+
     /// Whether you want to execute hub modelling code. Explicitly passing a `revision` is
     /// encouraged when loading a model with custom code to ensure no malicious code has been
     /// contributed in a newer revision.
@@ -669,6 +692,7 @@ fn shard_manager(
     quantize: Option<Quantization>,
     speculate: Option<usize>,
     dtype: Option<Dtype>,
+    kv_cache_dtype: Option<KVCacheDtype>,
     trust_remote_code: bool,
     uds_path: String,
     rank: usize,
@@ -742,6 +766,11 @@ fn shard_manager(
         shard_args.push(dtype.to_string())
     }
 
+    if let Some(kv_cache_dtype) = kv_cache_dtype {
+        shard_args.push("--kv-cache-dtype".to_string());
+        shard_args.push(kv_cache_dtype.to_string())
+    }
+
     // Model optional revision
     if let Some(revision) = revision {
         shard_args.push("--revision".to_string());
@@ -915,19 +944,19 @@ fn shard_manager(
         }
     });
     // We read stdin in another thread as it seems that lines() can block in some cases
-    thread::spawn(move || {
-        let mut stdin = io::stdin(); // We get `Stdin` here.
-        loop {
-            let mut buffer = vec![0; 4096];
-            if let Ok(n) = stdin.read(&mut buffer) {
-                if n > 0 {
-                    let _ = pstdin.write_all(&buffer[..n]);
-                } else {
-                    break;
+    if LevelFilter::current() >= tracing::Level::DEBUG {
+        thread::spawn(move || {
+            let mut stdin = io::stdin(); // We get `Stdin` here.
+            loop {
+                let mut buffer = vec![0; 4096];
+                if let Ok(n) = stdin.read(&mut buffer) {
+                    if n > 0 {
+                        let _ = pstdin.write_all(&buffer[..n]);
+                    }
                 }
             }
-        }
-    });
+        });
+    }
 
     let mut ready = false;
     let start_time = Instant::now();
@@ -1302,6 +1331,7 @@ fn spawn_shards(
         let otlp_service_name = args.otlp_service_name.clone();
         let speculate = args.speculate;
         let dtype = args.dtype;
+        let kv_cache_dtype = args.kv_cache_dtype;
         let trust_remote_code = args.trust_remote_code;
         let master_port = args.master_port;
         let disable_custom_kernels = args.disable_custom_kernels;
@@ -1320,6 +1350,7 @@ fn spawn_shards(
                 quantize,
                 speculate,
                 dtype,
+                kv_cache_dtype,
                 trust_remote_code,
                 uds_path,
                 rank,
@@ -1812,14 +1843,37 @@ fn main() -> Result<(), LauncherError> {
             if adapter.contains('=') {
                 continue;
             }
-            download_convert_model(
-                adapter,
-                None,
-                args.trust_remote_code,
-                args.huggingface_hub_cache.as_deref(),
-                args.weights_cache_override.as_deref(),
-                running.clone(),
-            )?;
+
+            let adapter = adapter.trim();
+
+            // check if adapter has more than 1 '@'
+            if adapter.matches('@').count() > 1 {
+                return Err(LauncherError::ArgumentValidation(format!(
+                    "Invalid LoRA adapter format: {}",
+                    adapter
+                )));
+            }
+
+            // capture adapter_id, path, revision in format of adapter_id=path@revision
+            let re = Regex::new(r"^([^=@]+)(?:=([^@]+))?(?:@(.+))?$").unwrap();
+            if let Some(caps) = re.captures(adapter) {
+                let adapter_id = caps.get(1).map_or("", |m| m.as_str());
+                let revision = caps.get(3).map(|m| m.as_str());
+
+                download_convert_model(
+                    adapter_id,
+                    revision,
+                    args.trust_remote_code,
+                    args.huggingface_hub_cache.as_deref(),
+                    args.weights_cache_override.as_deref(),
+                    running.clone(),
+                )?;
+            } else {
+                return Err(LauncherError::ArgumentValidation(format!(
+                    "Invalid LoRA adapter format: {}",
+                    adapter
+                )));
+            }
         }
     }
 
diff --git a/nix/docker.nix b/nix/docker.nix
new file mode 100644
index 00000000..c4b1d899
--- /dev/null
+++ b/nix/docker.nix
@@ -0,0 +1,23 @@
+{
+  dockerTools,
+  cacert,
+  text-generation-inference,
+  stream ? false,
+}:
+
+let
+  build = if stream then dockerTools.streamLayeredImage else dockerTools.buildLayeredImage;
+in
+build {
+  name = "tgi-docker";
+  tag = "latest";
+  config = {
+    EntryPoint = [ "${text-generation-inference}/bin/text-generation-inference" ];
+    Env = [
+      "HF_HOME=/data"
+      "PORT=80"
+    ];
+
+  };
+  contents = [ cacert ];
+}
diff --git a/nix/impure-shell.nix b/nix/impure-shell.nix
index a4dad4ba..abed544a 100644
--- a/nix/impure-shell.nix
+++ b/nix/impure-shell.nix
@@ -1,5 +1,7 @@
 {
   mkShell,
+  black,
+  isort,
   openssl,
   pkg-config,
   protobuf,
@@ -14,6 +16,8 @@
 mkShell {
   buildInputs =
     [
+      black
+      isort
       openssl.dev
       pkg-config
       (rust-bin.stable.latest.default.override {
diff --git a/nix/overlay.nix b/nix/overlay.nix
new file mode 100644
index 00000000..f7b9b8c2
--- /dev/null
+++ b/nix/overlay.nix
@@ -0,0 +1,41 @@
+final: prev: {
+  # You can use this overlay to temporarily override packages for
+  # development. For permanent overrides, it's better to do this in
+  # our package flake:
+  #
+  # https://github.com/huggingface/text-generation-inference-nix
+  #
+  # Note that overriding packages that are in the transitive closure
+  # of many other packages (e.g. transformers) will require a large
+  # rebuild.
+
+  pythonPackagesExtensions = prev.pythonPackagesExtensions ++ [
+    (
+      python-self: python-super: with python-self; {
+        # Python package override example:
+        #   transformers = python-super.transformers.overrideAttrs (
+        #     _: _: {
+        #       src = final.fetchFromGitHub {
+        #         owner = "huggingface";
+        #         repo = "transformers";
+        #         rev = "2bd4d5897dc73e8b172832070a6f9e567a0df017";
+        #         hash = "sha256-JOIpKH9ssDEfI2Tf15e0iPKtThJwQ9GxMvRAnm+M2Pg=";
+        #       };
+        #    }
+        # );
+      }
+    )
+  ];
+
+  # Non-python package override example:
+  #
+  # ripgrep = prev.ripgrep.overrideAttrs (
+  #    _: _: {
+  #      src = final.fetchFromGitHub {
+  #      owner = "BurntSushi";
+  #      repo = "ripgrep";
+  #      rev = "79cbe89deb1151e703f4d91b19af9cdcc128b765";
+  #      hash = "sha256-JPTM2KNmGMb+/jOfK3X7OM1wnN+3TU35SJOIcqmp3mg=";
+  #   };
+  # });
+}
diff --git a/router/src/config.rs b/router/src/config.rs
index 5d0be9c8..1a20c40b 100644
--- a/router/src/config.rs
+++ b/router/src/config.rs
@@ -146,6 +146,7 @@ pub enum Config {
     ClipVisionModel(ClipVisionModel),
     Mistral,
     Idefics,
+    Mllama,
     Idefics2(Idefics2),
     Ssm,
     GptBigcode,
@@ -159,6 +160,7 @@ pub enum Config {
     #[serde(rename = "phi-msft")]
     PhiMsft,
     Phi3,
+    PhiMoe,
     Llama,
     Baichuan,
     Paligemma(Paligemma),
diff --git a/router/src/infer/chat_template.rs b/router/src/infer/chat_template.rs
index a736fc12..1071d0ba 100644
--- a/router/src/infer/chat_template.rs
+++ b/router/src/infer/chat_template.rs
@@ -29,7 +29,7 @@ impl ChatTemplate {
         env.set_unknown_method_callback(pycompat::unknown_method_callback);
         let template_str = template.into_boxed_str();
         env.add_function("raise_exception", raise_exception);
-        tracing::debug!("Loading template: {:#?}", template_str);
+        tracing::debug!("Loading template: {}", template_str);
 
         // leaking env and template_str as read-only, static resources for performance.
         let template = Box::leak(env)
diff --git a/router/src/infer/mod.rs b/router/src/infer/mod.rs
index 1c9d5620..896f4f43 100644
--- a/router/src/infer/mod.rs
+++ b/router/src/infer/mod.rs
@@ -355,6 +355,8 @@ pub enum InferError {
     MissingTemplateVariable(String),
     #[error("Tool error: {0}")]
     ToolError(String),
+    #[error("Stream event serialization error")]
+    StreamSerializationError(String),
 }
 
 impl InferError {
@@ -368,6 +370,7 @@ impl InferError {
             InferError::TemplateError(_) => "template_error",
             InferError::MissingTemplateVariable(_) => "missing_template_variable",
             InferError::ToolError(_) => "tool_error",
+            InferError::StreamSerializationError(_) => "stream_serialization_error",
         }
     }
 }
diff --git a/router/src/infer/tool_grammar.rs b/router/src/infer/tool_grammar.rs
index 4fe15720..f86205fb 100644
--- a/router/src/infer/tool_grammar.rs
+++ b/router/src/infer/tool_grammar.rs
@@ -31,32 +31,29 @@ impl ToolGrammar {
 
         let mut tools = tools.clone();
 
-        // add the notify_error function to the tools
-        let notify_error = Tool {
+        // add the no_tool function to the tools
+        let no_tool = Tool {
             r#type: "function".to_string(),
             function: FunctionDefinition {
-                name: "notify_error".to_string(),
-                description: Some("Notify an error or issue".to_string()),
+                name: "no_tool".to_string(),
+                description: Some("Open ened response with no specific tool selected".to_string()),
                 arguments: json!({
                     "type": "object",
                     "properties": {
-                        "error": {
+                        "content": {
                             "type": "string",
-                            "description": "The error or issue to notify"
+                            "description": "The response content",
                         }
                     },
-                    "required": ["error"]
+                    "required": ["content"]
                 }),
             },
         };
-        tools.push(notify_error);
+        tools.push(no_tool);
 
         // if tools are provided and no tool_choice we default to the OneOf
         let tools_to_use = match tool_choice {
-            ToolType::FunctionName(name) => {
-                vec![Self::find_tool_by_name(&tools, &name)?]
-            }
-            ToolType::Function { function } => {
+            ToolType::Function(function) => {
                 vec![Self::find_tool_by_name(&tools, &function.name)?]
             }
             ToolType::OneOf => tools.clone(),
diff --git a/router/src/lib.rs b/router/src/lib.rs
index 0901bafa..b29c9395 100644
--- a/router/src/lib.rs
+++ b/router/src/lib.rs
@@ -957,12 +957,18 @@ pub fn default_tool_prompt() -> String {
 }
 
 #[derive(Clone, Debug, Deserialize, PartialEq, Serialize, ToSchema)]
-#[serde(untagged)]
+#[schema(example = "auto")]
+/// Controls which (if any) tool is called by the model.
 pub enum ToolType {
+    /// Means the model can pick between generating a message or calling one or more tools.
+    #[schema(rename = "auto")]
     OneOf,
-    FunctionName(String),
-    Function { function: FunctionName },
+    /// Means the model will not call any tool and instead generates a message.
+    #[schema(rename = "none")]
     NoTool,
+    /// Forces the model to call a specific tool.
+    #[schema(rename = "function")]
+    Function(FunctionName),
 }
 
 #[derive(Debug, Clone, PartialEq, Serialize, Deserialize, ToSchema)]
@@ -977,6 +983,7 @@ pub struct ToolChoice(pub Option<ToolType>);
 #[derive(Deserialize)]
 #[serde(untagged)]
 enum ToolTypeDeserializer {
+    Null,
     String(String),
     ToolType(ToolType),
 }
@@ -984,10 +991,11 @@ enum ToolTypeDeserializer {
 impl From<ToolTypeDeserializer> for ToolChoice {
     fn from(value: ToolTypeDeserializer) -> Self {
         match value {
+            ToolTypeDeserializer::Null => ToolChoice(None),
             ToolTypeDeserializer::String(s) => match s.as_str() {
                 "none" => ToolChoice(Some(ToolType::NoTool)),
                 "auto" => ToolChoice(Some(ToolType::OneOf)),
-                _ => ToolChoice(Some(ToolType::FunctionName(s))),
+                _ => ToolChoice(Some(ToolType::Function(FunctionName { name: s }))),
             },
             ToolTypeDeserializer::ToolType(tool_type) => ToolChoice(Some(tool_type)),
         }
diff --git a/router/src/server.rs b/router/src/server.rs
index cc896f99..5e6e6960 100644
--- a/router/src/server.rs
+++ b/router/src/server.rs
@@ -42,6 +42,7 @@ use hf_hub::{Cache, Repo, RepoType};
 use http::header::AUTHORIZATION;
 use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle};
 use pyo3::types::IntoPyDict;
+use regex::Regex;
 use serde_json::Value;
 use std::convert::Infallible;
 use std::fs::File;
@@ -452,12 +453,20 @@ async fn generate_stream(
     Sse<impl Stream<Item = Result<Event, Infallible>>>,
 ) {
     let span = tracing::Span::current();
-    let on_message_callback = |stream_token: StreamResponse| {
-        let event = Event::default();
-        event.json_data(stream_token).unwrap()
-    };
     let (headers, response_stream) =
-        generate_stream_internal(infer, compute_type, Json(req), on_message_callback, span).await;
+        generate_stream_internal(infer, compute_type, Json(req), span).await;
+
+    let response_stream = async_stream::stream! {
+        let mut response_stream = Box::pin(response_stream);
+        while let Some(raw_event) = response_stream.next().await {
+            yield Ok(raw_event.map_or_else(Event::from, |token| {
+                Event::default()
+                    .json_data(token)
+                    .unwrap_or_else(|e| InferError::StreamSerializationError(e.to_string()).into())
+            }));
+        }
+    };
+
     let sse = Sse::new(response_stream).keep_alive(KeepAlive::default());
     (headers, sse)
 }
@@ -466,9 +475,11 @@ async fn generate_stream_internal(
     infer: Infer,
     ComputeType(compute_type): ComputeType,
     Json(req): Json<GenerateRequest>,
-    on_message_callback: impl Fn(StreamResponse) -> Event,
     span: tracing::Span,
-) -> (HeaderMap, impl Stream<Item = Result<Event, Infallible>>) {
+) -> (
+    HeaderMap,
+    impl Stream<Item = Result<StreamResponse, InferError>>,
+) {
     let start_time = Instant::now();
     metrics::counter!("tgi_request_count").increment(1);
 
@@ -500,12 +511,12 @@ async fn generate_stream_internal(
             let err = InferError::from(ValidationError::BestOfStream);
             metrics::counter!("tgi_request_failure", "err" => "validation").increment(1);
             tracing::error!("{err}");
-            yield Ok(Event::from(err));
+            yield Err(err);
         } else if req.parameters.decoder_input_details {
             let err = InferError::from(ValidationError::PrefillDetailsStream);
             metrics::counter!("tgi_request_failure", "err" => "validation").increment(1);
             tracing::error!("{err}");
-            yield Ok(Event::from(err));
+            yield Err(err);
         } else {
             match infer.generate_stream(req).instrument(info_span!(parent: &span, "async_stream")).await {
                 // Keep permit as long as generate_stream lives
@@ -535,8 +546,7 @@ async fn generate_stream_internal(
                                             generated_text: None,
                                             details: None,
                                         };
-                                        let event = on_message_callback(stream_token);
-                                        yield Ok(event);
+                                        yield Ok(stream_token);
                                     }
                                     // Yield event for last token and compute timings
                                     InferStreamResponse::End {
@@ -600,9 +610,7 @@ async fn generate_stream_internal(
                                             details
                                         };
 
-
-                                        let event = on_message_callback(stream_token);
-                                        yield Ok(event);
+                                        yield Ok(stream_token);
                                         break;
                                     }
                                 }
@@ -610,7 +618,7 @@ async fn generate_stream_internal(
                             // yield error
                             Err(err) => {
                                 error = true;
-                                yield Ok(Event::from(err));
+                                yield Err(err);
                                 break;
                             }
                         }
@@ -619,7 +627,7 @@ async fn generate_stream_internal(
                 // yield error
                 Err(err) => {
                     error = true;
-                    yield Ok(Event::from(err));
+                    yield Err(err);
                 }
             }
             // Check if generation reached the end
@@ -628,7 +636,7 @@ async fn generate_stream_internal(
                 let err = InferError::IncompleteGenerationStream;
                 metrics::counter!("tgi_request_failure", "err" => "incomplete").increment(1);
                 tracing::error!("{err}");
-                yield Ok(Event::from(err));
+                yield Err(err);
             }
         }
     };
@@ -771,75 +779,85 @@ async fn completions(
 
             // Create a future for each generate_stream_internal call.
             let generate_future = async move {
-                let on_message_callback = move |stream_token: StreamResponse| {
-                    let event = Event::default();
-
-                    let current_time = std::time::SystemTime::now()
-                        .duration_since(std::time::UNIX_EPOCH)
-                        .unwrap_or_else(|_| std::time::Duration::from_secs(0))
-                        .as_secs();
-
-                    let message = match stream_token.details {
-                        Some(details) => {
-                            let completion_tokens = details.generated_tokens;
-                            let prompt_tokens = details.input_length;
-                            let total_tokens = prompt_tokens + completion_tokens;
-
-                            Completion::Final(CompletionFinal {
-                                id: String::new(),
-                                created: current_time,
-                                model: model_id.clone(),
-                                system_fingerprint: system_fingerprint.clone(),
-                                choices: vec![CompletionComplete {
-                                    finish_reason: details.finish_reason.to_string(),
-                                    index: index as u32,
-                                    logprobs: None,
-                                    text: stream_token.token.text,
-                                }],
-                                usage: Usage {
-                                    prompt_tokens,
-                                    completion_tokens,
-                                    total_tokens,
-                                },
-                            })
-                        }
-                        None => Completion::Chunk(Chunk {
-                            id: String::new(),
-                            created: current_time,
-                            choices: vec![CompletionComplete {
-                                finish_reason: String::new(),
-                                index: index as u32,
-                                logprobs: None,
-                                text: stream_token.token.text,
-                            }],
-                            model: model_id.clone(),
-                            system_fingerprint: system_fingerprint.clone(),
-                        }),
-                    };
-
-                    event
-                        .json_data(message)
-                        .unwrap_or_else(|_e| Event::default())
-                };
-
                 let (header_tx, header_rx) = oneshot::channel();
                 let (sse_tx, sse_rx) = tokio::sync::mpsc::unbounded_channel();
 
                 tokio::spawn(async move {
-                    let (header_map, sse) = generate_stream_internal(
+                    let (headers, response_stream) = generate_stream_internal(
                         infer_clone.clone(),
                         compute_type_clone.clone(),
                         Json(generate_request),
-                        on_message_callback,
                         span_clone.clone(),
                     )
                     .await;
 
+                    let response_stream = async_stream::stream! {
+                        let mut response_stream = Box::pin(response_stream);
+
+                        while let Some(stream_token) = response_stream.next().await {
+                            match stream_token {
+                                Ok(stream_token) => {
+                                    let event = Event::default();
+
+                                    let current_time = std::time::SystemTime::now()
+                                        .duration_since(std::time::UNIX_EPOCH)
+                                        .unwrap_or_else(|_| std::time::Duration::from_secs(0))
+                                        .as_secs();
+
+                                    let message = match stream_token.details {
+                                        Some(details) => {
+                                            let completion_tokens = details.generated_tokens;
+                                            let prompt_tokens = details.input_length;
+                                            let total_tokens = prompt_tokens + completion_tokens;
+
+                                            Completion::Final(CompletionFinal {
+                                                id: String::new(),
+                                                created: current_time,
+                                                model: model_id.clone(),
+                                                system_fingerprint: system_fingerprint.clone(),
+                                                choices: vec![CompletionComplete {
+                                                    finish_reason: details.finish_reason.to_string(),
+                                                    index: index as u32,
+                                                    logprobs: None,
+                                                    text: stream_token.token.text,
+                                                }],
+                                                usage: Usage {
+                                                    prompt_tokens,
+                                                    completion_tokens,
+                                                    total_tokens,
+                                                },
+                                            })
+                                        }
+                                        None => Completion::Chunk(Chunk {
+                                            id: String::new(),
+                                            created: current_time,
+                                            choices: vec![CompletionComplete {
+                                                finish_reason: String::new(),
+                                                index: index as u32,
+                                                logprobs: None,
+                                                text: stream_token.token.text,
+                                            }],
+                                            model: model_id.clone(),
+                                            system_fingerprint: system_fingerprint.clone(),
+                                        }),
+                                    };
+
+                                    let event = event
+                                        .json_data(message)
+                                        .unwrap_or_else(|_e| Event::default());
+
+                                    yield Ok(event);
+                                }
+                                Err(err) => yield Ok(Event::from(err)),
+                            }
+                        }
+                    };
+
                     // send and dont wait for response
-                    let _ = header_tx.send(header_map);
+                    let _ = header_tx.send(headers);
 
                     // pin an emit messages to the sse_tx
-                    let mut sse = Box::pin(sse);
+                    let mut sse = Box::pin(response_stream);
                     while let Some(event) = sse.next().await {
                         if sse_tx.send(event).is_err() {
                             tracing::error!("Failed to send event. Receiver dropped.");
@@ -1072,6 +1090,84 @@ async fn completions(
     }
 }
 
+enum StreamState {
+    Buffering,
+    BufferTrailing,
+    Content { skip_close_quote: bool },
+}
+
+/// Convert a StreamResponse into an Event to be sent over SSE
+fn create_event_from_stream_token(
+    stream_token: &StreamResponse,
+    logprobs: bool,
+    stream_options: Option<StreamOptions>,
+    inner_using_tools: bool,
+    system_fingerprint: String,
+    model_id: String,
+) -> Event {
+    let event = Event::default();
+    let current_time = std::time::SystemTime::now()
+        .duration_since(std::time::UNIX_EPOCH)
+        .unwrap_or_else(|_| std::time::Duration::from_secs(0))
+        .as_secs();
+
+    let logprobs = logprobs.then(|| {
+        ChatCompletionLogprobs::from((stream_token.token.clone(), stream_token.top_tokens.clone()))
+    });
+
+    // replace the content with the tool calls if grammar is present
+    let (content, tool_calls) = if inner_using_tools {
+        (None, Some(vec![stream_token.token.text.clone()]))
+    } else {
+        let content = if !stream_token.token.special {
+            Some(stream_token.token.text.clone())
+        } else {
+            None
+        };
+
+        (content, None)
+    };
+
+    let (usage, finish_reason) = match &stream_token.details {
+        Some(details) => {
+            let usage = if stream_options
+                .as_ref()
+                .map(|s| s.include_usage)
+                .unwrap_or(false)
+            {
+                let completion_tokens = details.generated_tokens;
+                let prompt_tokens = details.input_length;
+                let total_tokens = prompt_tokens + completion_tokens;
+                Some(Usage {
+                    completion_tokens,
+                    prompt_tokens,
+                    total_tokens,
+                })
+            } else {
+                None
+            };
+            (usage, Some(details.finish_reason.format(true)))
+        }
+        None => (None, None),
+    };
+
+    let chat_complete = CompletionType::ChatCompletionChunk(ChatCompletionChunk::new(
+        model_id.clone(),
+        system_fingerprint.clone(),
+        content,
+        tool_calls,
+        current_time,
+        logprobs,
+        finish_reason,
+        usage,
+    ));
+
+    event.json_data(chat_complete).unwrap_or_else(|e| {
+        println!("Failed to serialize ChatCompletionChunk: {:?}", e);
+        Event::default()
+    })
+}
+
 /// Generate tokens
 #[utoipa::path(
 post,
@@ -1128,88 +1224,135 @@ async fn chat_completions(
     // static values that will be returned in all cases
     let model_id = info.model_id.clone();
     let system_fingerprint = format!("{}-{}", info.version, info.docker_label.unwrap_or("native"));
-
     // switch on stream
     if stream {
-        // pass this callback to the stream generation and build the required event structure
-        let on_message_callback = move |stream_token: StreamResponse| {
-            let event = Event::default();
+        let (headers, response_stream) =
+            generate_stream_internal(infer, compute_type, Json(generate_request), span).await;
 
-            let current_time = std::time::SystemTime::now()
-                .duration_since(std::time::UNIX_EPOCH)
-                .unwrap_or_else(|_| std::time::Duration::from_secs(0))
-                .as_secs();
-
-            let logprobs = logprobs.then(|| {
-                ChatCompletionLogprobs::from((stream_token.token.clone(), stream_token.top_tokens))
-            });
-
-            // replace the content with the tool calls if grammar is present
-            let (content, tool_calls) = if using_tools {
-                (None, Some(vec![stream_token.token.text]))
-            } else {
-                let content = if !stream_token.token.special {
-                    Some(stream_token.token.text)
-                } else {
-                    None
-                };
-
-                (content, None)
-            };
-
-            let (usage, finish_reason) = match stream_token.details {
-                Some(details) => {
-                    let usage = if stream_options
-                        .as_ref()
-                        .map(|s| s.include_usage)
-                        .unwrap_or(false)
-                    {
-                        let completion_tokens = details.generated_tokens;
-                        let prompt_tokens = details.input_length;
-                        let total_tokens = prompt_tokens + completion_tokens;
-                        Some(Usage {
-                            completion_tokens,
-                            prompt_tokens,
-                            total_tokens,
-                        })
-                    } else {
-                        None
-                    };
-                    (usage, Some(details.finish_reason.format(true)))
-                }
-                None => (None, None),
-            };
-            event
-                .json_data(CompletionType::ChatCompletionChunk(
-                    ChatCompletionChunk::new(
-                        model_id.clone(),
-                        system_fingerprint.clone(),
-                        content,
-                        tool_calls,
-                        current_time,
-                        logprobs,
-                        finish_reason,
-                        usage,
-                    ),
+        // regex to match any function name
+        let function_regex = match Regex::new(r#"\{"function":\{"_name":"([^"]+)""#) {
+            Ok(regex) => regex,
+            Err(e) => {
+                return Err((
+                    StatusCode::INTERNAL_SERVER_ERROR,
+                    Json(ErrorResponse {
+                        error: format!("Failed to compile regex: {}", e),
+                        error_type: "regex".to_string(),
+                    }),
                 ))
-                .unwrap_or_else(|e| {
-                    println!("Failed to serialize ChatCompletionChunk: {:?}", e);
-                    Event::default()
-                })
+            }
         };
 
-        let (headers, response_stream) = generate_stream_internal(
-            infer,
-            compute_type,
-            Json(generate_request),
-            on_message_callback,
-            span,
-        )
-        .await;
+        let response_stream = async_stream::stream! {
+            let mut response_stream = Box::pin(response_stream);
+            let mut buffer = Vec::new();
+            let mut json_buffer = String::new();
+            let mut state = if using_tools {
+                StreamState::Buffering
+            } else {
+                StreamState::Content {
+                    skip_close_quote: false,
+                }
+            };
+            let mut response_as_tool = using_tools;
+            while let Some(result) = response_stream.next().await {
+                if let Ok(stream_token) = result {
+                    let token_text = &stream_token.token.text.clone();
+                    match state {
+                        StreamState::Buffering => {
+                            json_buffer.push_str(&token_text.replace(" ", ""));
+                            buffer.push(stream_token);
+                            if let Some(captures) = function_regex.captures(&json_buffer) {
+                                let function_name = captures[1].to_string();
+                                if function_name == "no_tool" {
+                                    state = StreamState::BufferTrailing;
+                                    response_as_tool = false;
+                                    buffer.clear();
+                                    json_buffer.clear();
+                                } else {
+                                    state = StreamState::Content {
+                                        skip_close_quote: false,
+                                    };
+                                    // send all the buffered messages
+                                    for stream_token in &buffer {
+                                        let event = create_event_from_stream_token(
+                                            stream_token,
+                                            logprobs,
+                                            stream_options.clone(),
+                                            response_as_tool,
+                                            system_fingerprint.clone(),
+                                            model_id.clone(),
+                                        );
+                                        yield Ok::<Event, Infallible>(event);
+                                    }
+                                }
+                            }
+                        }
+                        // if we skipped sending the buffer we need to avoid sending the following json key and quotes
+                        StreamState::BufferTrailing => {
+                            let infix_text = "\"content\":\"";
+                            json_buffer.push_str(&token_text.replace(" ", ""));
+                            // keep capturing until we find the infix text
+                            match json_buffer.find(infix_text) {
+                                Some(content_key_index) => {
+                                    json_buffer =
+                                        json_buffer[content_key_index + infix_text.len()..].to_string();
+                                }
+                                None => {
+                                    continue;
+                                }
+                            }
+                            // if there is leftover text after removing the infix text, we need to send it
+                            if !json_buffer.is_empty() {
+                                let event = Event::default();
+                                let current_time = std::time::SystemTime::now()
+                                    .duration_since(std::time::UNIX_EPOCH)
+                                    .unwrap_or_else(|_| std::time::Duration::from_secs(0))
+                                    .as_secs();
+                                let chat_complete =
+                                    CompletionType::ChatCompletionChunk(ChatCompletionChunk::new(
+                                        model_id.clone(),
+                                        system_fingerprint.clone(),
+                                        Some(json_buffer.clone()),
+                                        None,
+                                        current_time,
+                                        None,
+                                        None,
+                                        None,
+                                    ));
+                                yield Ok(event.json_data(chat_complete).unwrap_or_else(|e| {
+                                    InferError::StreamSerializationError(e.to_string()).into()
+                                }));
+                            }
+                            // cleanup the buffers
+                            buffer.clear();
+                            json_buffer.clear();
+                            state = StreamState::Content {
+                                skip_close_quote: true,
+                            };
+                        }
+                        StreamState::Content { skip_close_quote } => {
+                            if skip_close_quote && token_text.contains('"') {
+                                break;
+                            }
 
-        let response_stream = response_stream.chain(futures::stream::once(async {
-            Ok(Event::default().data("[DONE]"))
-        }));
+                            // send the content
+                            let event = create_event_from_stream_token(
+                                &stream_token,
+                                logprobs,
+                                stream_options.clone(),
+                                response_as_tool,
+                                system_fingerprint.clone(),
+                                model_id.clone(),
+                            );
+
+                            yield Ok::<Event, Infallible>(event);
+                        }
+                    }
+                }
+            }
+            yield Ok::<Event, Infallible>(Event::default().data("[DONE]"));
+        };
 
         let sse = Sse::new(response_stream).keep_alive(KeepAlive::default());
         Ok((headers, sse).into_response())
@@ -1246,17 +1389,33 @@ async fn chat_completions(
             if let Value::Object(ref mut props) = arguments {
                 props.remove("_name");
             }
-
-            let tool_calls = vec![ToolCall {
-                id: "0".to_string(),
-                r#type: "function".to_string(),
-                function: FunctionDefinition {
-                    description: None,
-                    name,
-                    arguments,
-                },
-            }];
-            (Some(tool_calls), None)
+            match name.as_str() {
+                "no_tool" => {
+                    // parse the content message
+                    let content_message = arguments
+                        .get("content")
+                        .and_then(Value::as_str)
+                        .ok_or_else(|| {
+                            InferError::ToolError(
+                                "No `content` found in generated text".to_string(),
+                            )
+                        })?
+                        .to_string();
+                    (None, Some(content_message))
+                }
+                _ => {
+                    let tool_calls = vec![ToolCall {
+                        id: "0".to_string(),
+                        r#type: "function".to_string(),
+                        function: FunctionDefinition {
+                            description: None,
+                            name,
+                            arguments,
+                        },
+                    }];
+                    (Some(tool_calls), None)
+                }
+            }
         } else {
             (None, Some(generation.generated_text))
         };
@@ -1937,6 +2096,11 @@ async fn start(
         metrics::Unit::Count,
         "Maximum tokens for the current batch"
     );
+    metrics::describe_gauge!(
+        "tgi_batch_total_tokens",
+        metrics::Unit::Count,
+        "Maximum amount of tokens in total."
+    );
     metrics::describe_histogram!(
         "tgi_request_max_new_tokens",
         metrics::Unit::Count,
@@ -2318,6 +2482,7 @@ impl From<InferError> for (StatusCode, Json<ErrorResponse>) {
             InferError::TemplateError(_) => StatusCode::UNPROCESSABLE_ENTITY,
             InferError::MissingTemplateVariable(_) => StatusCode::UNPROCESSABLE_ENTITY,
             InferError::ToolError(_) => StatusCode::UNPROCESSABLE_ENTITY,
+            InferError::StreamSerializationError(_) => StatusCode::INTERNAL_SERVER_ERROR,
         };
 
         (
@@ -2495,8 +2660,8 @@ mod tests {
         );
 
         assert!(result.is_ok());
-        let (inputs, _grammar, using_tools) = result.unwrap();
+        let (inputs, _grammar, using_tools) = result.expect("Failed to prepare chat input");
         assert_eq!(using_tools, true);
-        assert_eq!(inputs, "<s>[AVAILABLE_TOOLS] [{\"type\": \"function\", \"function\": {\"arguments\": {\"properties\":{\"format\":{\"description\":\"The temperature unit to use. Infer this from the users location.\",\"enum\":[\"celsius\",\"fahrenheit\"],\"type\":\"string\"},\"location\":{\"description\":\"The city and state, e.g. San Francisco, CA\",\"type\":\"string\"}},\"required\":[\"location\",\"format\"],\"type\":\"object\"}, \"description\": \"Get the current weather\", \"name\": \"get_current_weather\"}}, {\"type\": \"function\", \"function\": {\"arguments\": {\"properties\":{\"error\":{\"description\":\"The error or issue to notify\",\"type\":\"string\"}},\"required\":[\"error\"],\"type\":\"object\"}, \"description\": \"Notify an error or issue\", \"name\": \"notify_error\"}}][/AVAILABLE_TOOLS][INST] What is the weather like in New York?\n---\nGiven the functions available, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. Respond in the format {name: function name, parameters: dictionary of argument name and its value}.Do not use variables.[/INST]".to_string());
+        assert_eq!(inputs, "<s>[AVAILABLE_TOOLS] [{\"type\": \"function\", \"function\": {\"arguments\": {\"properties\":{\"format\":{\"description\":\"The temperature unit to use. Infer this from the users location.\",\"enum\":[\"celsius\",\"fahrenheit\"],\"type\":\"string\"},\"location\":{\"description\":\"The city and state, e.g. San Francisco, CA\",\"type\":\"string\"}},\"required\":[\"location\",\"format\"],\"type\":\"object\"}, \"description\": \"Get the current weather\", \"name\": \"get_current_weather\"}}, {\"type\": \"function\", \"function\": {\"arguments\": {\"properties\":{\"content\":{\"description\":\"The response content\",\"type\":\"string\"}},\"required\":[\"content\"],\"type\":\"object\"}, \"description\": \"Open ened response with no specific tool selected\", \"name\": \"no_tool\"}}][/AVAILABLE_TOOLS][INST] What is the weather like in New York?\n---\nGiven the functions available, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. Respond in the format {name: function name, parameters: dictionary of argument name and its value}.Do not use variables.[/INST]".to_string());
     }
 }
diff --git a/router/src/validation.rs b/router/src/validation.rs
index 92491d88..85b4220b 100644
--- a/router/src/validation.rs
+++ b/router/src/validation.rs
@@ -567,6 +567,7 @@ fn image_tokens(
     use HubPreprocessorConfig::*;
     match config {
         Idefics => "<image>".to_string(),
+        Mllama => "<|image|>".to_string(),
         Idefics2(config) => {
             const FAKE: &str = "<fake_token_around_image>";
             const IMAGE: &str = "<image>";
@@ -618,7 +619,7 @@ fn prepare_input(
     use Config::*;
     static RE: Lazy<Regex> = Lazy::new(|| Regex::new(r"!\[\]\([^\)]*\)").unwrap());
     let (tokenizer_query, input_chunks) = match config {
-        Some(config @ (Idefics | Idefics2(_) | Paligemma(_) | LlavaNext(_))) => {
+        Some(config @ (Idefics | Mllama | Idefics2(_) | Paligemma(_) | LlavaNext(_))) => {
             let mut input_chunks = Vec::new();
             let mut tokenizer_query = String::with_capacity(inputs.len());
             let mut start = 0;
diff --git a/rust-toolchain.toml b/rust-toolchain.toml
index f392b161..12d58532 100644
--- a/rust-toolchain.toml
+++ b/rust-toolchain.toml
@@ -1,5 +1,5 @@
 [toolchain]
 # Released on: June 13, 2024
 # https://releases.rs/docs/1.79.0/
-channel = "1.80.0"
+channel = "1.80.1"
 components = ["rustfmt", "clippy"]
diff --git a/server/Makefile-flash-att-v2 b/server/Makefile-flash-att-v2
index dbddd0f4..a9cdf782 100644
--- a/server/Makefile-flash-att-v2
+++ b/server/Makefile-flash-att-v2
@@ -1,5 +1,5 @@
 flash_att_v2_commit_cuda := v2.6.1
-flash_att_v2_commit_rocm := 2554f490101742ccdc56620a938f847f61754be6
+flash_att_v2_commit_rocm := 2092111b9f975b3347c652ff7fabd431130256c4
 
 build-flash-attention-v2-cuda:
 	pip install -U packaging wheel
@@ -11,7 +11,7 @@ install-flash-attention-v2-cuda: build-flash-attention-v2-cuda
 build-flash-attention-v2-rocm:
 	if [ ! -d 'flash-attention-v2' ]; then \
 		pip install -U packaging ninja  --no-cache-dir && \
-		git clone https://github.com/ROCm/flash-attention.git flash-attention-v2 && \
+		git clone https://github.com/mht-sharma/flash-attention.git flash-attention-v2 && \
 		cd flash-attention-v2 && git fetch && git checkout $(flash_att_v2_commit_rocm) && \
 		git submodule update --init --recursive && GPU_ARCHS="gfx90a;gfx942" PYTORCH_ROCM_ARCH="gfx90a;gfx942" python setup.py build; \
 	fi
diff --git a/server/Makefile-vllm b/server/Makefile-vllm
index f1f80529..18dcc4a0 100644
--- a/server/Makefile-vllm
+++ b/server/Makefile-vllm
@@ -1,5 +1,5 @@
 commit_cuda := d243e9dc7e2c9c2e36a4150ec8e64809cb55c01b
-commit_rocm := c6ee53b1be97e3bbc791b95f22827501297f8921
+commit_rocm := 4e0929e6e4fa0a3d09d358715c288020ea9dc247
 build-vllm-cuda:
 	if [ ! -d 'vllm' ]; then \
 		pip install -U ninja packaging --no-cache-dir && \
@@ -13,7 +13,7 @@ install-vllm-cuda: build-vllm-cuda
 build-vllm-rocm:
 	if [ ! -d 'vllm' ]; then \
 		pip install -U ninja packaging --no-cache-dir && \
-		git clone https://github.com/fxmarty/rocm-vllm.git vllm; \
+		git clone https://github.com/mht-sharma/vllm.git vllm; \
 	fi
 	cd vllm && git fetch && git checkout $(commit_rocm) &&  \
 	PYTORCH_ROCM_ARCH="gfx90a;gfx942" python setup.py build
diff --git a/server/exllama_kernels/setup.py b/server/exllama_kernels/setup.py
index 987d181e..cc307bf0 100644
--- a/server/exllama_kernels/setup.py
+++ b/server/exllama_kernels/setup.py
@@ -1,5 +1,17 @@
 from setuptools import setup
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+import torch
+
+extra_cuda_cflags = []
+extra_cflags = []
+if torch.version.hip:
+    extra_cflags = ["-DLEGACY_HIPBLAS_DIRECT=ON"]
+    extra_cuda_cflags = ["-DLEGACY_HIPBLAS_DIRECT=ON"]
+
+extra_compile_args = {
+    "cxx": extra_cflags,
+    "nvcc": extra_cuda_cflags,
+}
 
 setup(
     name="exllama_kernels",
@@ -13,6 +25,7 @@ setup(
                 "exllama_kernels/cuda_func/q4_matmul.cu",
                 "exllama_kernels/cuda_func/q4_matrix.cu",
             ],
+            extra_compile_args=extra_compile_args,
         )
     ],
     cmdclass={"build_ext": BuildExtension},
diff --git a/server/exllamav2_kernels/setup.py b/server/exllamav2_kernels/setup.py
index 4a16b546..56ffa973 100644
--- a/server/exllamav2_kernels/setup.py
+++ b/server/exllamav2_kernels/setup.py
@@ -3,11 +3,13 @@ from torch.utils.cpp_extension import BuildExtension, CUDAExtension
 import torch
 
 extra_cuda_cflags = ["-lineinfo", "-O3"]
-
+extra_cflags = []
 if torch.version.hip:
-    extra_cuda_cflags += ["-DHIPBLAS_USE_HIP_HALF"]
+    extra_cflags = ["-DLEGACY_HIPBLAS_DIRECT=ON"]
+    extra_cuda_cflags += ["-DHIPBLAS_USE_HIP_HALF", "-DLEGACY_HIPBLAS_DIRECT=ON"]
 
 extra_compile_args = {
+    "cxx": extra_cflags,
     "nvcc": extra_cuda_cflags,
 }
 
diff --git a/server/poetry.lock b/server/poetry.lock
index 8d0e31f8..08f74999 100644
--- a/server/poetry.lock
+++ b/server/poetry.lock
@@ -30,101 +30,128 @@ test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "py
 test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
 testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.0"
+description = "Happy Eyeballs for asyncio"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"},
+    {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"},
+]
+
 [[package]]
 name = "aiohttp"
-version = "3.9.5"
+version = "3.10.6"
 description = "Async http client/server framework (asyncio)"
 optional = true
 python-versions = ">=3.8"
 files = [
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+    {file = "aiohttp-3.10.6.tar.gz", hash = "sha256:d2578ef941be0c2ba58f6f421a703527d08427237ed45ecb091fed6f83305336"},
 ]
 
 [package.dependencies]
+aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
 async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-yarl = ">=1.0,<2.0"
+yarl = ">=1.12.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "brotlicffi"]
+speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
@@ -164,32 +191,32 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.2.0"
+version = "24.2.0"
 description = "Classes Without Boilerplate"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
-    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
+    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
 ]
 
 [package.extras]
-cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[tests]", "pre-commit"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
-tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "bitsandbytes"
-version = "0.43.1"
+version = "0.43.3"
 description = "k-bit optimizers and matrix multiplication routines."
 optional = true
 python-versions = "*"
 files = [
-    {file = "bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:a81c826d576d6d691c7b4a7491c8fdc0f37f769795d6ca2e54afa605d2c260a3"},
-    {file = "bitsandbytes-0.43.1-py3-none-win_amd64.whl", hash = "sha256:52c1c7189a6ca006555a9663e544e75f40520a97a26e075411f9f9aca0771fcd"},
+    {file = "bitsandbytes-0.43.3-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:cc99507c352be0715098b2c7577b690dd158972dc4ea10c7495bac104c7c79f0"},
+    {file = "bitsandbytes-0.43.3-py3-none-win_amd64.whl", hash = "sha256:257f6552f2144748a84e6c44e1f7a98f3da888f675ed74e18fd7f7eb13c6cafa"},
 ]
 
 [package.dependencies]
@@ -202,13 +229,13 @@ test = ["scipy"]
 
 [[package]]
 name = "certifi"
-version = "2024.7.4"
+version = "2024.8.30"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
-    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
+    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
+    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
 ]
 
 [[package]]
@@ -348,27 +375,26 @@ files = [
 
 [[package]]
 name = "datasets"
-version = "2.20.0"
+version = "2.21.0"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = true
 python-versions = ">=3.8.0"
 files = [
-    {file = "datasets-2.20.0-py3-none-any.whl", hash = "sha256:76ac02e3bdfff824492e20678f0b6b1b6d080515957fe834b00c2ba8d6b18e5e"},
-    {file = "datasets-2.20.0.tar.gz", hash = "sha256:3c4dbcd27e0f642b9d41d20ff2efa721a5e04b32b2ca4009e0fc9139e324553f"},
+    {file = "datasets-2.21.0-py3-none-any.whl", hash = "sha256:25e4e097110ce28824b746a107727ada94024cba11db8bc588d468414692b65a"},
+    {file = "datasets-2.21.0.tar.gz", hash = "sha256:998f85a8460f1bd982e5bd058f8a0808eef424249e3df1e8cdd594ccd0dc8ba2"},
 ]
 
 [package.dependencies]
 aiohttp = "*"
 dill = ">=0.3.0,<0.3.9"
 filelock = "*"
-fsspec = {version = ">=2023.1.0,<=2024.5.0", extras = ["http"]}
+fsspec = {version = ">=2023.1.0,<=2024.6.1", extras = ["http"]}
 huggingface-hub = ">=0.21.2"
 multiprocess = "*"
 numpy = ">=1.17"
 packaging = "*"
 pandas = "*"
 pyarrow = ">=15.0.0"
-pyarrow-hotfix = "*"
 pyyaml = ">=5.1"
 requests = ">=2.32.2"
 tqdm = ">=4.66.3"
@@ -376,17 +402,18 @@ xxhash = "*"
 
 [package.extras]
 apache-beam = ["apache-beam (>=2.26.0)"]
-audio = ["librosa", "soundfile (>=0.12.1)"]
+audio = ["librosa", "soundfile (>=0.12.1)", "soxr (>=0.4.0)"]
 benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
-dev = ["Pillow (>=9.4.0)", "absl-py", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+dev = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "transformers (>=4.42.0)", "typing-extensions (>=4.6.1)", "zstandard"]
 docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"]
 jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
-metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
+metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk (<3.8.2)", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
 quality = ["ruff (>=0.3.0)"]
 s3 = ["s3fs"]
 tensorflow = ["tensorflow (>=2.6.0)"]
 tensorflow-gpu = ["tensorflow (>=2.6.0)"]
-tests = ["Pillow (>=9.4.0)", "absl-py", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+tests = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "transformers (>=4.42.0)", "typing-extensions (>=4.6.1)", "zstandard"]
+tests-numpy2 = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tiktoken", "torch (>=2.0.0)", "typing-extensions (>=4.6.1)", "zstandard"]
 torch = ["torch"]
 vision = ["Pillow (>=9.4.0)"]
 
@@ -460,19 +487,19 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.15.4"
+version = "3.16.1"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.15.4-py3-none-any.whl", hash = "sha256:6ca1fffae96225dab4c6eaf1c4f4f28cd2568d3ec2a44e15a08520504de468e7"},
-    {file = "filelock-3.15.4.tar.gz", hash = "sha256:2207938cbc1844345cb01a5a95524dae30f0ce089eba5b00378295a17e3e90cb"},
+    {file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"},
+    {file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"]
-typing = ["typing-extensions (>=4.8)"]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4.1)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
+typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "frozenlist"
@@ -562,13 +589,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.5.0"
+version = "2024.6.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2024.5.0-py3-none-any.whl", hash = "sha256:e0fdbc446d67e182f49a70b82cf7889028a63588fde6b222521f10937b2b670c"},
-    {file = "fsspec-2024.5.0.tar.gz", hash = "sha256:1d021b0b0f933e3b3029ed808eb400c08ba101ca2de4b3483fbc9ca23fcee94a"},
+    {file = "fsspec-2024.6.1-py3-none-any.whl", hash = "sha256:3cb443f8bcd2efb31295a5b9fdb02aee81d8452c80d28f97a6d0959e6cee101e"},
+    {file = "fsspec-2024.6.1.tar.gz", hash = "sha256:fad7d7e209dd4c1208e3bbfda706620e0da5142bebbd9c384afb95b07e798e49"},
 ]
 
 [package.dependencies]
@@ -580,6 +607,7 @@ adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
 dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
 full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
@@ -603,13 +631,13 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.63.2"
+version = "1.65.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "googleapis-common-protos-1.63.2.tar.gz", hash = "sha256:27c5abdffc4911f28101e635de1533fb4cfd2c37fbaa9174587c799fac90aa87"},
-    {file = "googleapis_common_protos-1.63.2-py2.py3-none-any.whl", hash = "sha256:27a2499c7e8aff199665b22741997e485eccc8645aa9176c7c988e6fae507945"},
+    {file = "googleapis_common_protos-1.65.0-py2.py3-none-any.whl", hash = "sha256:2972e6c496f435b92590fd54045060867f3fe9be2c82ab148fc8885035479a63"},
+    {file = "googleapis_common_protos-1.65.0.tar.gz", hash = "sha256:334a29d07cddc3aa01dee4988f9afd9b2916ee2ff49d6b757155dc0d197852c0"},
 ]
 
 [package.dependencies]
@@ -637,158 +665,152 @@ testing = ["protobuf (>=4.21.9)"]
 
 [[package]]
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 optional = false
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 ]
 
 [package.extras]
-protobuf = ["grpcio-tools (>=1.65.1)"]
+protobuf = ["grpcio-tools (>=1.66.1)"]
 
 [[package]]
 name = "grpcio-reflection"
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 description = "Standard Protobuf Reflection Service for gRPC"
 optional = false
 python-versions = ">=3.6"
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 ]
 
 [package.dependencies]
-grpcio = ">=1.62.2"
+grpcio = ">=1.62.3"
 protobuf = ">=4.21.6,<5.0dev"
 setuptools = "*"
 
@@ -892,15 +914,18 @@ typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "t
 
 [[package]]
 name = "idna"
-version = "3.7"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 files = [
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 ]
 
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
 [[package]]
 name = "importlib-metadata"
 version = "7.1.0"
@@ -1007,13 +1032,13 @@ referencing = ">=0.31.0"
 
 [[package]]
 name = "lark"
-version = "1.1.9"
+version = "1.2.2"
 description = "a modern parsing library"
 optional = true
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1244,12 +1269,12 @@ files = [
 
 [[package]]
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-version = "0.3.1"
+version = "0.6.0"
 description = "MoE kernels"
 optional = true
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -1259,16 +1284,16 @@ triton = "*"
 
 [package.source]
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+url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl"
 
 [[package]]
 name = "moe-kernels"
-version = "0.3.1"
+version = "0.6.0"
 description = "MoE kernels"
 optional = true
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -1278,16 +1303,16 @@ triton = "*"
 
 [package.source]
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-url = "https://github.com/danieldk/moe-kernels/releases/download/v0.3.1/moe_kernels-0.3.1+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
+url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
 
 [[package]]
 name = "moe-kernels"
-version = "0.3.1"
+version = "0.6.0"
 description = "MoE kernels"
 optional = true
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -1297,16 +1322,16 @@ triton = "*"
 
 [package.source]
 type = "url"
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+url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl"
 
 [[package]]
 name = "moe-kernels"
-version = "0.3.1"
+version = "0.6.0"
 description = "MoE kernels"
 optional = true
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -1316,7 +1341,7 @@ triton = "*"
 
 [package.source]
 type = "url"
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+url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl"
 
 [[package]]
 name = "mpmath"
@@ -1337,103 +1362,108 @@ tests = ["pytest (>=4.6)"]
 
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+version = "6.1.0"
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-python-versions = ">=3.7"
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 [[package]]
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 [[package]]
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 [[package]]
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 [[package]]
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 [package.extras]
@@ -2936,73 +3000,85 @@ test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "po
 
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 ]
 
 [package.dependencies]
@@ -3216,6 +3292,7 @@ nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"
 nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+setuptools = "*"
 sympy = "*"
 triton = {version = "3.0.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
 typing-extensions = ">=4.8.0"
@@ -3226,13 +3303,13 @@ optree = ["optree (>=0.11.0)"]
 
 [[package]]
 name = "tqdm"
-version = "4.66.4"
+version = "4.66.5"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3246,13 +3323,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "transformers"
-version = "4.43.1"
+version = "4.45.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
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 ]
 
 [package.dependencies]
@@ -3264,21 +3341,21 @@ pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
 safetensors = ">=0.4.1"
-tokenizers = ">=0.19,<0.20"
+tokenizers = ">=0.20,<0.21"
 tqdm = ">=4.27"
 
 [package.extras]
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+    {file = "yarl-1.12.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6af871f70cfd5b528bd322c65793b5fd5659858cdfaa35fbe563fb99b667ed1f"},
+    {file = "yarl-1.12.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c3e4e1f7b08d1ec6b685ccd3e2d762219c550164fbf524498532e39f9413436e"},
+    {file = "yarl-1.12.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9a7ee79183f0b17dcede8b6723e7da2ded529cf159a878214be9a5d3098f5b1e"},
+    {file = "yarl-1.12.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:96c8ff1e1dd680e38af0887927cab407a4e51d84a5f02ae3d6eb87233036c763"},
+    {file = "yarl-1.12.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7e9905fc2dc1319e4c39837b906a024cf71b1261cc66b0cd89678f779c0c61f5"},
+    {file = "yarl-1.12.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:01549468858b87d36f967c97d02e6e54106f444aeb947ed76f8f71f85ed07cec"},
+    {file = "yarl-1.12.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:96b34830bd6825ca0220bf005ea99ac83eb9ce51301ddb882dcf613ae6cd95fb"},
+    {file = "yarl-1.12.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:2aee7594d2c2221c717a8e394bbed4740029df4c0211ceb0f04815686e99c795"},
+    {file = "yarl-1.12.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:15871130439ad10abb25a4631120d60391aa762b85fcab971411e556247210a0"},
+    {file = "yarl-1.12.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:838dde2cb570cfbb4cab8a876a0974e8b90973ea40b3ac27a79b8a74c8a2db15"},
+    {file = "yarl-1.12.1-cp39-cp39-win32.whl", hash = "sha256:eacbcf30efaca7dc5cb264228ffecdb95fdb1e715b1ec937c0ce6b734161e0c8"},
+    {file = "yarl-1.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:76a59d1b63de859398bc7764c860a769499511463c1232155061fe0147f13e01"},
+    {file = "yarl-1.12.1-py3-none-any.whl", hash = "sha256:dc3192a81ecd5ff954cecd690327badd5a84d00b877e1573f7c9097ce13e5bfb"},
+    {file = "yarl-1.12.1.tar.gz", hash = "sha256:5b860055199aec8d6fe4dcee3c5196ce506ca198a50aab0059ffd26e8e815828"},
 ]
 
 [package.dependencies]
@@ -3708,18 +3803,22 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zipp"
-version = "3.19.2"
+version = "3.20.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
-    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
+    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
 ]
 
 [package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
 
 [extras]
 accelerate = ["accelerate"]
@@ -3734,4 +3833,4 @@ torch = ["torch"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "0ff7a244a409b616490cb238995bbe28dedf67ccb8855edafa2b71ee2e777dbd"
+content-hash = "be5965e4e13fed32347983dba1690661f3e59a50d59fa26d4f2a8345418dd5a1"
diff --git a/server/pyproject.toml b/server/pyproject.toml
index 6bdd2385..6ea4718d 100644
--- a/server/pyproject.toml
+++ b/server/pyproject.toml
@@ -23,10 +23,10 @@ opentelemetry-api = "^1.25.0"
 opentelemetry-exporter-otlp = "^1.25.0"
 opentelemetry-instrumentation-grpc = "^0.46b0"
 hf-transfer = "^0.1.2"
-sentencepiece = "^0.1.97"
-tokenizers = "^0.19.1"
+sentencepiece = "^0.2"
+tokenizers = "^0.20"
 huggingface-hub = "^0.23"
-transformers = "^4.43"
+transformers = "^4.45"
 einops = "^0.6.1"
 texttable = { version = "^1.6.7", optional = true }
 datasets = { version = "^2.14.0", optional = true }
@@ -47,10 +47,10 @@ marlin-kernels = [
   { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
 ]
 moe-kernels = [
-  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.3.1/moe_kernels-0.3.1+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
-  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.3.1/moe_kernels-0.3.1+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
-  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.3.1/moe_kernels-0.3.1+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
-  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.3.1/moe_kernels-0.3.1+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
+  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
+  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
+  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
+  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
 ]
 rich = "^13.7.1"
 
@@ -82,3 +82,6 @@ requires = [
     "poetry-core>=1.0.0",
 ]
 build-backend = "poetry.core.masonry.api"
+
+[tool.isort]
+profile = "black"
diff --git a/server/requirements_cuda.txt b/server/requirements_cuda.txt
index eb521bd6..5de75b6b 100644
--- a/server/requirements_cuda.txt
+++ b/server/requirements_cuda.txt
@@ -1,19 +1,19 @@
-certifi==2024.7.4 ; python_version >= "3.9" and python_version < "3.13"
+certifi==2024.8.30 ; python_version >= "3.9" and python_version < "3.13"
 charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
 click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
 colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
 deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
 einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
-filelock==3.15.4 ; python_version >= "3.9" and python_version < "3.13"
-fsspec==2024.5.0 ; python_version >= "3.9" and python_version < "3.13"
-googleapis-common-protos==1.63.2 ; python_version >= "3.9" and python_version < "3.13"
+filelock==3.16.1 ; python_version >= "3.9" and python_version < "3.13"
+fsspec==2024.6.1 ; python_version >= "3.9" and python_version < "3.13"
+googleapis-common-protos==1.65.0 ; python_version >= "3.9" and python_version < "3.13"
 grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.65.1 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-reflection==1.62.3 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-status==1.62.3 ; python_version >= "3.9" and python_version < "3.13"
+grpcio==1.66.1 ; python_version >= "3.9" and python_version < "3.13"
 hf-transfer==0.1.8 ; python_version >= "3.9" and python_version < "3.13"
 huggingface-hub==0.23.5 ; python_version >= "3.9" and python_version < "3.13"
-idna==3.7 ; python_version >= "3.9" and python_version < "3.13"
+idna==3.10 ; python_version >= "3.9" and python_version < "3.13"
 importlib-metadata==7.1.0 ; python_version >= "3.9" and python_version < "3.13"
 loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
 markdown-it-py==3.0.0 ; python_version >= "3.9" and python_version < "3.13"
@@ -32,23 +32,23 @@ opentelemetry-semantic-conventions==0.46b0 ; python_version >= "3.9" and python_
 packaging==24.1 ; python_version >= "3.9" and python_version < "3.13"
 pillow==10.4.0 ; python_version >= "3.9" and python_version < "3.13"
 prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
-protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13"
+protobuf==4.25.5 ; python_version >= "3.9" and python_version < "3.13"
 py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < "3.13"
 pygments==2.18.0 ; python_version >= "3.9" and python_version < "3.13"
-pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
-regex==2024.5.15 ; python_version >= "3.9" and python_version < "3.13"
+pyyaml==6.0.2 ; python_version >= "3.9" and python_version < "3.13"
+regex==2024.9.11 ; python_version >= "3.9" and python_version < "3.13"
 requests==2.32.3 ; python_version >= "3.9" and python_version < "3.13"
-rich==13.7.1 ; python_version >= "3.9" and python_version < "3.13"
-safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13"
+rich==13.8.1 ; python_version >= "3.9" and python_version < "3.13"
+safetensors==0.4.5 ; python_version >= "3.9" and python_version < "3.13"
 scipy==1.13.1 ; python_version >= "3.9" and python_version < "3.13"
-sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
-setuptools==71.1.0 ; python_version >= "3.9" and python_version < "3.13"
-tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
-tqdm==4.66.4 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.43.1 ; python_version >= "3.9" and python_version < "3.13"
+sentencepiece==0.2.0 ; python_version >= "3.9" and python_version < "3.13"
+setuptools==75.1.0 ; python_version >= "3.9" and python_version < "3.13"
+tokenizers==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
+tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13"
+transformers==4.45.0 ; python_version >= "3.9" and python_version < "3.13"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
 typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13"
-urllib3==2.2.2 ; python_version >= "3.9" and python_version < "3.13"
+urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13"
 win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
 wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
-zipp==3.19.2 ; python_version >= "3.9" and python_version < "3.13"
+zipp==3.20.2 ; python_version >= "3.9" and python_version < "3.13"
diff --git a/server/requirements_intel.txt b/server/requirements_intel.txt
index eb521bd6..5de75b6b 100644
--- a/server/requirements_intel.txt
+++ b/server/requirements_intel.txt
@@ -1,19 +1,19 @@
-certifi==2024.7.4 ; python_version >= "3.9" and python_version < "3.13"
+certifi==2024.8.30 ; python_version >= "3.9" and python_version < "3.13"
 charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
 click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
 colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
 deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
 einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
-filelock==3.15.4 ; python_version >= "3.9" and python_version < "3.13"
-fsspec==2024.5.0 ; python_version >= "3.9" and python_version < "3.13"
-googleapis-common-protos==1.63.2 ; python_version >= "3.9" and python_version < "3.13"
+filelock==3.16.1 ; python_version >= "3.9" and python_version < "3.13"
+fsspec==2024.6.1 ; python_version >= "3.9" and python_version < "3.13"
+googleapis-common-protos==1.65.0 ; python_version >= "3.9" and python_version < "3.13"
 grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.65.1 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-reflection==1.62.3 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-status==1.62.3 ; python_version >= "3.9" and python_version < "3.13"
+grpcio==1.66.1 ; python_version >= "3.9" and python_version < "3.13"
 hf-transfer==0.1.8 ; python_version >= "3.9" and python_version < "3.13"
 huggingface-hub==0.23.5 ; python_version >= "3.9" and python_version < "3.13"
-idna==3.7 ; python_version >= "3.9" and python_version < "3.13"
+idna==3.10 ; python_version >= "3.9" and python_version < "3.13"
 importlib-metadata==7.1.0 ; python_version >= "3.9" and python_version < "3.13"
 loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
 markdown-it-py==3.0.0 ; python_version >= "3.9" and python_version < "3.13"
@@ -32,23 +32,23 @@ opentelemetry-semantic-conventions==0.46b0 ; python_version >= "3.9" and python_
 packaging==24.1 ; python_version >= "3.9" and python_version < "3.13"
 pillow==10.4.0 ; python_version >= "3.9" and python_version < "3.13"
 prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
-protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13"
+protobuf==4.25.5 ; python_version >= "3.9" and python_version < "3.13"
 py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < "3.13"
 pygments==2.18.0 ; python_version >= "3.9" and python_version < "3.13"
-pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
-regex==2024.5.15 ; python_version >= "3.9" and python_version < "3.13"
+pyyaml==6.0.2 ; python_version >= "3.9" and python_version < "3.13"
+regex==2024.9.11 ; python_version >= "3.9" and python_version < "3.13"
 requests==2.32.3 ; python_version >= "3.9" and python_version < "3.13"
-rich==13.7.1 ; python_version >= "3.9" and python_version < "3.13"
-safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13"
+rich==13.8.1 ; python_version >= "3.9" and python_version < "3.13"
+safetensors==0.4.5 ; python_version >= "3.9" and python_version < "3.13"
 scipy==1.13.1 ; python_version >= "3.9" and python_version < "3.13"
-sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
-setuptools==71.1.0 ; python_version >= "3.9" and python_version < "3.13"
-tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
-tqdm==4.66.4 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.43.1 ; python_version >= "3.9" and python_version < "3.13"
+sentencepiece==0.2.0 ; python_version >= "3.9" and python_version < "3.13"
+setuptools==75.1.0 ; python_version >= "3.9" and python_version < "3.13"
+tokenizers==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
+tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13"
+transformers==4.45.0 ; python_version >= "3.9" and python_version < "3.13"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
 typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13"
-urllib3==2.2.2 ; python_version >= "3.9" and python_version < "3.13"
+urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13"
 win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
 wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
-zipp==3.19.2 ; python_version >= "3.9" and python_version < "3.13"
+zipp==3.20.2 ; python_version >= "3.9" and python_version < "3.13"
diff --git a/server/requirements_rocm.txt b/server/requirements_rocm.txt
index eb521bd6..5de75b6b 100644
--- a/server/requirements_rocm.txt
+++ b/server/requirements_rocm.txt
@@ -1,19 +1,19 @@
-certifi==2024.7.4 ; python_version >= "3.9" and python_version < "3.13"
+certifi==2024.8.30 ; python_version >= "3.9" and python_version < "3.13"
 charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
 click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
 colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
 deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
 einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
-filelock==3.15.4 ; python_version >= "3.9" and python_version < "3.13"
-fsspec==2024.5.0 ; python_version >= "3.9" and python_version < "3.13"
-googleapis-common-protos==1.63.2 ; python_version >= "3.9" and python_version < "3.13"
+filelock==3.16.1 ; python_version >= "3.9" and python_version < "3.13"
+fsspec==2024.6.1 ; python_version >= "3.9" and python_version < "3.13"
+googleapis-common-protos==1.65.0 ; python_version >= "3.9" and python_version < "3.13"
 grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.65.1 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-reflection==1.62.3 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-status==1.62.3 ; python_version >= "3.9" and python_version < "3.13"
+grpcio==1.66.1 ; python_version >= "3.9" and python_version < "3.13"
 hf-transfer==0.1.8 ; python_version >= "3.9" and python_version < "3.13"
 huggingface-hub==0.23.5 ; python_version >= "3.9" and python_version < "3.13"
-idna==3.7 ; python_version >= "3.9" and python_version < "3.13"
+idna==3.10 ; python_version >= "3.9" and python_version < "3.13"
 importlib-metadata==7.1.0 ; python_version >= "3.9" and python_version < "3.13"
 loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
 markdown-it-py==3.0.0 ; python_version >= "3.9" and python_version < "3.13"
@@ -32,23 +32,23 @@ opentelemetry-semantic-conventions==0.46b0 ; python_version >= "3.9" and python_
 packaging==24.1 ; python_version >= "3.9" and python_version < "3.13"
 pillow==10.4.0 ; python_version >= "3.9" and python_version < "3.13"
 prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
-protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13"
+protobuf==4.25.5 ; python_version >= "3.9" and python_version < "3.13"
 py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < "3.13"
 pygments==2.18.0 ; python_version >= "3.9" and python_version < "3.13"
-pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
-regex==2024.5.15 ; python_version >= "3.9" and python_version < "3.13"
+pyyaml==6.0.2 ; python_version >= "3.9" and python_version < "3.13"
+regex==2024.9.11 ; python_version >= "3.9" and python_version < "3.13"
 requests==2.32.3 ; python_version >= "3.9" and python_version < "3.13"
-rich==13.7.1 ; python_version >= "3.9" and python_version < "3.13"
-safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13"
+rich==13.8.1 ; python_version >= "3.9" and python_version < "3.13"
+safetensors==0.4.5 ; python_version >= "3.9" and python_version < "3.13"
 scipy==1.13.1 ; python_version >= "3.9" and python_version < "3.13"
-sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
-setuptools==71.1.0 ; python_version >= "3.9" and python_version < "3.13"
-tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
-tqdm==4.66.4 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.43.1 ; python_version >= "3.9" and python_version < "3.13"
+sentencepiece==0.2.0 ; python_version >= "3.9" and python_version < "3.13"
+setuptools==75.1.0 ; python_version >= "3.9" and python_version < "3.13"
+tokenizers==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
+tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13"
+transformers==4.45.0 ; python_version >= "3.9" and python_version < "3.13"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
 typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13"
-urllib3==2.2.2 ; python_version >= "3.9" and python_version < "3.13"
+urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13"
 win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
 wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
-zipp==3.19.2 ; python_version >= "3.9" and python_version < "3.13"
+zipp==3.20.2 ; python_version >= "3.9" and python_version < "3.13"
diff --git a/server/text_generation_server/cli.py b/server/text_generation_server/cli.py
index 10aa3a3b..db390234 100644
--- a/server/text_generation_server/cli.py
+++ b/server/text_generation_server/cli.py
@@ -30,6 +30,10 @@ class Dtype(str, Enum):
     bloat16 = "bfloat16"
 
 
+class KVCacheDtype(str, Enum):
+    fp8_e5m2 = "fp8_e5m2"
+
+
 @app.command()
 def serve(
     model_id: str,
@@ -38,6 +42,7 @@ def serve(
     quantize: Optional[Quantization] = None,
     speculate: Optional[int] = None,
     dtype: Optional[Dtype] = None,
+    kv_cache_dtype: Optional[KVCacheDtype] = None,
     trust_remote_code: bool = False,
     uds_path: Path = "/tmp/text-generation-server",
     logger_level: str = "INFO",
@@ -97,6 +102,7 @@ def serve(
     # Downgrade enum into str for easier management later on
     quantize = None if quantize is None else quantize.value
     dtype = None if dtype is None else dtype.value
+    kv_cache_dtype = None if kv_cache_dtype is None else kv_cache_dtype.value
     if dtype is not None and quantize not in {
         None,
         "bitsandbytes",
@@ -114,6 +120,7 @@ def serve(
         quantize,
         speculate,
         dtype,
+        kv_cache_dtype,
         trust_remote_code,
         uds_path,
         max_input_tokens,
diff --git a/server/text_generation_server/layers/attention/__init__.py b/server/text_generation_server/layers/attention/__init__.py
index 4f2b9807..cc7f0caa 100644
--- a/server/text_generation_server/layers/attention/__init__.py
+++ b/server/text_generation_server/layers/attention/__init__.py
@@ -1,37 +1,40 @@
-from text_generation_server.utils.import_utils import SYSTEM
 import os
 
+from text_generation_server.utils.import_utils import SYSTEM
+
 from .common import Seqlen
 
 if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
     raise ImportError("`USE_FLASH_ATTENTION` is false.")
 if SYSTEM == "cuda":
     from .cuda import (
+        PREFILL_IN_KV_CACHE,
+        SUPPORTS_WINDOWING,
         attention,
         paged_attention,
         reshape_and_cache,
-        SUPPORTS_WINDOWING,
-        PREFILL_IN_KV_CACHE,
     )
 elif SYSTEM == "rocm":
     from .rocm import (
+        PREFILL_IN_KV_CACHE,
+        SUPPORTS_WINDOWING,
         attention,
         paged_attention,
         reshape_and_cache,
-        PREFILL_IN_KV_CACHE,
-        SUPPORTS_WINDOWING,
     )
 elif SYSTEM == "ipex":
     from .ipex import (
+        PREFILL_IN_KV_CACHE,
+        SUPPORTS_WINDOWING,
         attention,
         paged_attention,
         reshape_and_cache,
-        PREFILL_IN_KV_CACHE,
-        SUPPORTS_WINDOWING,
     )
 else:
     raise ImportError(f"System {SYSTEM} doesn't support flash/paged attention")
 
+# KVCache needs `reshape_and_cache`, so ensure that it is defined already.
+from .kv_cache import KVCache
 
 __all__ = [
     "attention",
@@ -39,5 +42,6 @@ __all__ = [
     "reshape_and_cache",
     "PREFILL_IN_KV_CACHE",
     "SUPPORTS_WINDOWING",
+    "KVCache",
     "Seqlen",
 ]
diff --git a/server/text_generation_server/layers/attention/common.py b/server/text_generation_server/layers/attention/common.py
index 855f4dfc..c8ac0c2a 100644
--- a/server/text_generation_server/layers/attention/common.py
+++ b/server/text_generation_server/layers/attention/common.py
@@ -1,4 +1,5 @@
 from dataclasses import dataclass
+from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.models.globals import ATTENTION
 import torch
 from typing import Optional
@@ -65,5 +66,7 @@ else:
         max_k: int
 
         def clamp(self, max):
-            raise NotImplementedError("Not implemented seqlen for paged")
-            return Seqlen(torch.clamp(self.input_lengths, max=max))
+            if SYSTEM == "rocm":
+                return self
+            self.input_lengths = torch.clamp(self.input_lengths, max=max)
+            return self
diff --git a/server/text_generation_server/layers/attention/cuda.py b/server/text_generation_server/layers/attention/cuda.py
index 51af928d..cd3ea369 100644
--- a/server/text_generation_server/layers/attention/cuda.py
+++ b/server/text_generation_server/layers/attention/cuda.py
@@ -355,3 +355,11 @@ else:
 # have a configuration that requires flash-attention v1, which
 # does not support block tables.
 PREFILL_IN_KV_CACHE = ATTENTION != "paged" or V2
+
+__all__ = [
+    "PREFILL_IN_KV_CACHE",
+    "SUPPORTS_WINDOWING",
+    "attention",
+    "paged_attention",
+    "reshape_and_cache",
+]
diff --git a/server/text_generation_server/layers/attention/flashinfer.py b/server/text_generation_server/layers/attention/flashinfer.py
index 5f8954ea..d603c6f5 100644
--- a/server/text_generation_server/layers/attention/flashinfer.py
+++ b/server/text_generation_server/layers/attention/flashinfer.py
@@ -50,7 +50,8 @@ def use_prefill_with_paged_kv_state(
     num_kv_heads: int,
     head_size: int,
     page_size: int,
-    query_dtype: str = "float16",
+    dtype: torch.dtype,
+    window_left: int,
 ):
     """
     Context manager to set the active flashinfer prefill state to the given
@@ -90,8 +91,9 @@ def use_prefill_with_paged_kv_state(
             num_qo_heads=num_heads,
             num_kv_heads=num_kv_heads,
             head_dim=head_size,
-            q_data_type=query_dtype,
+            q_data_type=dtype,
             page_size=page_size,
+            window_left=window_left,
         )
         yield
     finally:
@@ -119,7 +121,8 @@ def use_prefill_state(
     num_heads: int,
     num_kv_heads: int,
     head_size: int,
-    query_dtype: str = "float16",
+    dtype: torch.dtype,
+    window_left: int,
 ):
     """
     Context manager to set the active flashinfer prefill state to the given
@@ -135,7 +138,8 @@ def use_prefill_state(
             num_qo_heads=num_heads,
             num_kv_heads=num_kv_heads,
             head_dim=head_size,
-            q_data_type=query_dtype,
+            q_data_type=dtype,
+            window_left=window_left,
         )
         yield
     finally:
@@ -200,7 +204,8 @@ def use_decode_state(
     num_kv_heads: int,
     head_size: int,
     page_size: int,
-    query_dtype: str = "float16",
+    dtype: torch.dtype,
+    window_left: int,
 ):
     """
     Context manager to set the active flashinfer decoding state to the given
@@ -235,7 +240,9 @@ def use_decode_state(
             num_kv_heads=num_kv_heads,
             head_dim=head_size,
             page_size=page_size,
-            q_data_type=query_dtype,
+            data_type=dtype,
+            q_data_type=dtype,
+            window_left=window_left,
         )
         yield
     finally:
diff --git a/server/text_generation_server/layers/attention/ipex.py b/server/text_generation_server/layers/attention/ipex.py
index 657c90af..131c9bb0 100644
--- a/server/text_generation_server/layers/attention/ipex.py
+++ b/server/text_generation_server/layers/attention/ipex.py
@@ -80,3 +80,12 @@ def paged_attention(
         None,
     )
     return out
+
+
+__all__ = [
+    "PREFILL_IN_KV_CACHE",
+    "SUPPORTS_WINDOWING",
+    "attention",
+    "paged_attention",
+    "reshape_and_cache",
+]
diff --git a/server/text_generation_server/layers/attention/kv_cache.py b/server/text_generation_server/layers/attention/kv_cache.py
new file mode 100644
index 00000000..3960c954
--- /dev/null
+++ b/server/text_generation_server/layers/attention/kv_cache.py
@@ -0,0 +1,119 @@
+from typing import Tuple
+
+import torch
+from text_generation_server.models.globals import ATTENTION, BLOCK_SIZE
+from text_generation_server.utils.import_utils import SYSTEM
+from text_generation_server.layers.attention import reshape_and_cache
+
+
+class KVCache:
+    """
+    Key-value cache for attention layers.
+    """
+
+    kv_cache: Tuple[torch.Tensor, torch.Tensor]
+
+    def __init__(
+        self,
+        *,
+        num_blocks: int,
+        num_heads: int,
+        head_size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ):
+        """Construct the key-value cache for a layer."""
+
+        if dtype == torch.float8_e5m2 and (
+            ATTENTION != "flashinfer" or SYSTEM != "cuda"
+        ):
+            raise ValueError(
+                "float8_e5m2 KV cache is currently only supported for flashinfer on CUDA"
+            )
+
+        element_size = torch.tensor([], dtype=dtype).element_size()
+        if SYSTEM == "ipex" and device.type == "xpu":
+            x = 1
+        else:
+            x = BLOCK_SIZE // element_size
+
+        if ATTENTION in {"flashdecoding", "flashinfer"}:
+            self.kv_cache = (
+                torch.empty(
+                    (num_blocks, BLOCK_SIZE, num_heads, head_size),
+                    dtype=dtype,
+                    device=device,
+                ),
+                torch.empty(
+                    (num_blocks, BLOCK_SIZE, num_heads, head_size),
+                    dtype=dtype,
+                    device=device,
+                ),
+            )
+        elif SYSTEM == "ipex" and device == torch.device("cpu"):
+            self.kv_cache = (
+                torch.empty(
+                    (num_blocks, num_heads, BLOCK_SIZE, head_size),
+                    dtype=dtype,
+                    device=device,
+                ),
+                torch.empty(
+                    (num_blocks, num_heads, BLOCK_SIZE, head_size),
+                    dtype=dtype,
+                    device=device,
+                ),
+            )
+        else:
+            self.kv_cache = (
+                torch.zeros(
+                    (num_blocks, num_heads, head_size // x, BLOCK_SIZE, x),
+                    dtype=dtype,
+                    device=device,
+                ),
+                torch.zeros(
+                    (num_blocks, num_heads, head_size, BLOCK_SIZE),
+                    dtype=dtype,
+                    device=device,
+                ),
+            )
+
+    @property
+    def key(self):
+        """Get the key cache."""
+
+        return self.kv_cache[0]
+
+    @property
+    def value(self):
+        """Get the value cache."""
+
+        return self.kv_cache[1]
+
+    def store(
+        self,
+        *,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        slots: torch.Tensor,
+    ):
+        """Store the key and value at the given slots."""
+
+        key_cache = self.kv_cache[0]
+        value_cache = self.kv_cache[1]
+
+        if ATTENTION in {"flashdecoding", "flashinfer"}:
+            # TODO: add scale
+            key = key.to(key_cache.dtype)
+            value = value.to(value_cache.dtype)
+            if key_cache.dtype == torch.float8_e5m2:
+                # Torch index_put does not support float8_e5m2 yet, so
+                # put as raw data instead.
+                key_cache = key_cache.view(torch.uint8)
+                value_cache = value_cache.view(torch.uint8)
+                key = key.view(torch.uint8)
+                value = value.view(torch.uint8)
+            shape = key_cache.shape
+            key_cache.view(-1, shape[-2], shape[-1])[slots] = key
+            value_cache.view(-1, shape[-2], shape[-1])[slots] = value
+        else:
+            reshape_and_cache(key, value, key_cache, value_cache, slots)
diff --git a/server/text_generation_server/layers/attention/rocm.py b/server/text_generation_server/layers/attention/rocm.py
index 9f24ac98..01d4685a 100644
--- a/server/text_generation_server/layers/attention/rocm.py
+++ b/server/text_generation_server/layers/attention/rocm.py
@@ -1,4 +1,5 @@
 import os
+from typing import Optional
 import torch
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.models.globals import ATTENTION
@@ -8,16 +9,28 @@ from loguru import logger
 
 major, minor = torch.cuda.get_device_capability()
 is_sm75 = major == 7 and minor == 5
-_PARTITION_SIZE = 512
+
+_PARTITION_SIZE_V1V2 = 512
+_PARTITION_SIZE_CUSTOM = 256
 
 use_triton = os.getenv("ROCM_USE_FLASH_ATTN_V2_TRITON", "").lower() in {"true", "1"}
 ENGINE = "triton" if use_triton else "ck"
 
-
 PREFILL_IN_KV_CACHE = False
 
+use_rocm_custom_paged_attn = os.getenv("ROCM_USE_CUSTOM_PAGED_ATTN", "1") != "0"
 try:
-    from vllm._C import cache_ops
+    if use_rocm_custom_paged_attn:
+        from vllm._custom_C import paged_attention_custom
+except ImportError as e:
+    log_master(
+        logger.info,
+        f"Custom Paged Attention not available. Complete error: {e}",
+    )
+    use_rocm_custom_paged_attn = False
+
+try:
+    import vllm._custom_ops as ops
 except Exception as e:
     raise ImportError(
         f"Could not import vllm paged attention. Make sure your installation is correct. Complete error: {e}"
@@ -36,9 +49,7 @@ def reshape_and_cache(
         key_cache.view(-1, shape[-2], shape[-1])[slots] = key
         value_cache.view(-1, shape[-2], shape[-1])[slots] = value
     else:
-        cache_ops.reshape_and_cache(
-            key, value, key_cache, value_cache, slots, "auto", 1.0
-        )
+        ops.reshape_and_cache(key, value, key_cache, value_cache, slots, "auto", 1.0)
 
 
 def paged_attention(
@@ -48,8 +59,9 @@ def paged_attention(
     kv_head_mapping: torch.Tensor,
     softmax_scale: float,
     block_tables: torch.Tensor,
-    input_lengths: Seqlen,
+    seqlen: Seqlen,
     max_s: int,
+    softcap: Optional[float] = None,
 ):
     # Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
     # Copyright 2023 The vLLM team. All rights
@@ -68,11 +80,31 @@ def paged_attention(
     # limitations under the License.
     #
 
+    if softcap is not None:
+        raise RuntimeError("Paged attention doesn't support softcapping")
+
     # value_cache => [num_blocks, num_heads, head_size, block_size]
     block_size = value_cache.shape[3]
     num_seqs, num_heads, head_size = query.shape
+
+    num_kv_heads = key_cache.shape[1]
+    gqa_ratio = num_heads // num_kv_heads
+    use_custom = (
+        use_rocm_custom_paged_attn
+        and (query.dtype == torch.half or query.dtype == torch.bfloat16)
+        and (head_size == 128 or head_size == 64)
+        and (block_size == 16 or block_size == 32)
+        and (gqa_ratio >= 1 and gqa_ratio <= 16)
+        and max_s <= 32768
+    )
+
+    if not use_custom:
+        _PARTITION_SIZE = _PARTITION_SIZE_V1V2
+    else:
+        _PARTITION_SIZE = _PARTITION_SIZE_CUSTOM
+
     max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
-    input_lengths = input_lengths.input_lengths
+    input_lengths = seqlen.input_lengths
 
     out = torch.empty_like(query)
 
@@ -81,9 +113,13 @@ def paged_attention(
     # V1 to avoid the overhead of reduction. Also, if the number of
     # sequences or heads is large, we use V1 since there is enough work
     # to parallelize.
-    from vllm._C import ops
+    import vllm._custom_ops as ops
 
-    use_v1 = max_s <= 8192 and (max_num_partitions == 1 or num_seqs * num_heads > 512)
+    use_v1 = (
+        max_s <= 8192
+        and (max_num_partitions == 1 or num_seqs * num_heads > 512)
+        and not use_custom
+    )
     if use_v1:
         ops.paged_attention_v1(
             out,
@@ -115,24 +151,44 @@ def paged_attention(
         )
         max_logits = torch.empty_like(exp_sums)
 
-        ops.paged_attention_v2(
-            out,
-            exp_sums,
-            max_logits,
-            tmp_output,
-            query,
-            key_cache,
-            value_cache,
-            kv_head_mapping,
-            softmax_scale,
-            block_tables,
-            input_lengths,
-            block_size,
-            max_s,
-            None,
-            "auto",
-            1.0,
-        )
+        if not use_custom:
+            ops.paged_attention_v2(
+                out,
+                exp_sums,
+                max_logits,
+                tmp_output,
+                query,
+                key_cache,
+                value_cache,
+                kv_head_mapping,
+                softmax_scale,
+                block_tables,
+                input_lengths,
+                block_size,
+                max_s,
+                None,
+                "auto",
+                1.0,
+            )
+        else:
+            paged_attention_custom(
+                out,
+                exp_sums,
+                max_logits,
+                tmp_output,
+                query,
+                key_cache,
+                value_cache,
+                num_kv_heads,
+                softmax_scale,
+                block_tables,
+                input_lengths,
+                block_size,
+                max_s,
+                None,
+                "auto",
+            )
+
     return out
 
 
@@ -175,13 +231,14 @@ if ENGINE == "ck":
 
     def attention(
         q,
-        k,
-        v,
-        cu_seqlens,
-        max_s,
-        softmax_scale,
-        window_size_left=-1,
-        causal=True,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        seqlen: Seqlen,
+        block_tables: torch.Tensor,
+        softmax_scale: float,
+        window_size_left: int = -1,
+        causal: bool = True,
+        softcap: float = 0.0,
     ):
         if window_size_left <= 0 and window_size_left != -1:
             raise ValueError("`window_size_left` must be > 0 or -1")
@@ -191,46 +248,57 @@ if ENGINE == "ck":
         # We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
         return flash_attn_2_cuda.varlen_fwd(
             q,
-            k,
-            v,
+            key_cache,
+            value_cache,
             out,
-            cu_seqlens,
-            cu_seqlens,
-            max_s,
-            max_s,
+            seqlen.cu_seqlen_q,
+            seqlen.cu_seqlen_q,
+            None,
+            None,
+            None,
+            None,
+            seqlen.max_q,
+            seqlen.max_k,
             0.0,
             softmax_scale,
             False,
             causal,
+            window_size_left,
+            0,
+            softcap,
             False,
             None,
-        )
+        )[0]
 
 elif ENGINE == "triton":
     from .flash_attn_triton import triton_attention
 
     def attention(
         q,
-        k,
-        v,
-        cu_seqlens,
-        max_s,
-        softmax_scale,
-        window_size_left=-1,
-        causal=True,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        seqlen: Seqlen,
+        block_tables: torch.Tensor,
+        softmax_scale: float,
+        window_size_left: int = -1,
+        causal: bool = True,
+        softcap: Optional[float] = None,
     ):
+        if softcap is not None:
+            raise NotImplementedError("softcap is only available with CK flash attn")
+
         out = torch.empty_like(q)
 
         # We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
         output, _ = triton_attention(
             q,
-            k,
-            v,
+            key_cache,
+            value_cache,
             out,
-            cu_seqlens,
-            cu_seqlens,
-            max_s,
-            max_s,
+            seqlen.cu_seqlen_q,
+            seqlen.cu_seqlen_q,
+            seqlen.max_q,
+            seqlen.max_k,
             causal,
             softmax_scale,
         )
@@ -238,3 +306,11 @@ elif ENGINE == "triton":
 
 else:
     raise RuntimeError(f"Unknown attention engine {ENGINE}")
+
+__all__ = [
+    "PREFILL_IN_KV_CACHE",
+    "SUPPORTS_WINDOWING",
+    "attention",
+    "paged_attention",
+    "reshape_and_cache",
+]
diff --git a/server/text_generation_server/layers/linear.py b/server/text_generation_server/layers/linear.py
index 12d7f83a..08306d57 100644
--- a/server/text_generation_server/layers/linear.py
+++ b/server/text_generation_server/layers/linear.py
@@ -1,12 +1,21 @@
 import torch
 from text_generation_server.utils.import_utils import SYSTEM
 from torch.nn import functional as F
+import os
 
 if SYSTEM == "rocm":
-    try:
-        from vllm import _custom_C
-    except Exception as e:
-        raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
+    ROCM_USE_SKINNY_GEMM = os.getenv("ROCM_USE_SKINNY_GEMM", "True").lower() in (
+        "true",
+        "1",
+    )
+
+    if ROCM_USE_SKINNY_GEMM:
+        try:
+            from vllm import _custom_C
+        except Exception as e:
+            raise ImportError(
+                f"Could not load `vllm._custom_C` for ROCm skinny gemm. Full error: {e}"
+            )
 
 
 class FastLinear(torch.nn.Module):
@@ -48,6 +57,14 @@ class FastLinearROCm(torch.nn.Module):
         else:
             self.bias = None
 
+        self.cu_count = torch.cuda.get_device_properties(
+            device="cuda"
+        ).multi_processor_count
+        self.use_skinny_gemm = (
+            ROCM_USE_SKINNY_GEMM
+            and "gfx1" not in torch.cuda.get_device_properties("cuda").gcnArchName
+        )
+
     @classmethod
     def load(cls, config, prefix: str, weights, bias: bool):
         weight = weights.get_tensor(f"{prefix}.weight")
@@ -61,7 +78,11 @@ class FastLinearROCm(torch.nn.Module):
         weight = self.weight
         bias = self.bias
 
-        if SYSTEM == "rocm" and inp.numel() // inp.shape[-1] == 1:
+        if (
+            self.use_skinny_gemm
+            and inp.dtype == torch.float16
+            and inp.shape[-1] % 8 == 0
+        ):
             batched = False
             inp_shape = inp.shape
 
@@ -69,13 +90,16 @@ class FastLinearROCm(torch.nn.Module):
                 inp = inp.view(-1, inp_shape[-1])
                 batched = True
 
-            m, k = weight.shape[0], inp_shape[1]
-            out = torch.empty(
-                inp_shape[0], weight.shape[0], dtype=inp.dtype, device="cuda"
-            )
-            if (k == 8192 and (m == 1280 or m == 7168)) or (k == 3584 and m == 8192):
-                _custom_C.LLMM1(weight, inp, out, 8)
-            elif k <= 8192 and k % 8 == 0 and m % 4 == 0:
+            m, n, k = weight.shape[0], inp_shape[0], inp_shape[1]
+            if m > 8 and n <= 4:
+                out = torch.empty(
+                    inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
+                )
+                _custom_C.wvSpltK(weight, inp, out, n, self.cu_count)
+            elif m % 4 == 0 and n == 1 and k <= 8192:
+                out = torch.empty(
+                    inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
+                )
                 _custom_C.LLMM1(weight, inp, out, 4)
             else:
                 out = F.linear(inp, weight)
diff --git a/server/text_generation_server/layers/marlin/gptq.py b/server/text_generation_server/layers/marlin/gptq.py
index c7663b60..7245431f 100644
--- a/server/text_generation_server/layers/marlin/gptq.py
+++ b/server/text_generation_server/layers/marlin/gptq.py
@@ -43,7 +43,7 @@ def can_use_gptq_marlin(
         and quant_method in {"awq", "gptq"}
         and bits in GPTQ_MARLIN_BITS
         and groupsize in GPTQ_MARLIN_GROUP_SIZES
-        # We only suppord asymmetric quantization for AWQ.
+        # We only support asymmetric quantization for AWQ.
         and (sym or quant_method == "awq")
     )
 
@@ -109,7 +109,6 @@ class GPTQMarlinWeightsLoader(WeightsLoader):
         prefix: str,
         block_sizes: Union[int, List[int]],
     ):
-
         try:
             qweight = weights.get_packed_sharded(
                 f"{prefix}.qweight", dim=1, block_sizes=block_sizes
@@ -352,7 +351,7 @@ def repack_gptq_for_marlin(
 
     scales = permute_scales(scales)
 
-    is_full_k = not (desc_act and sharded_infeatures)
+    is_full_k = not (desc_act and groupsize != -1 and sharded_infeatures)
 
     return GPTQMarlinWeight(
         qweight=repacked,
diff --git a/server/text_generation_server/layers/moe/__init__.py b/server/text_generation_server/layers/moe/__init__.py
index 3171af90..558d9ed9 100644
--- a/server/text_generation_server/layers/moe/__init__.py
+++ b/server/text_generation_server/layers/moe/__init__.py
@@ -10,16 +10,24 @@ from text_generation_server.layers import (
     TensorParallelRowLinear,
 )
 from text_generation_server.layers.fp8 import HybridFP8UnquantLoader
+from text_generation_server.layers.marlin import GPTQMarlinWeightsLoader
+from text_generation_server.layers.moe.gptq_marlin import (
+    GPTQMarlinSparseMoELayer,
+    can_use_marlin_moe_gemm,
+)
 from text_generation_server.layers.moe.unquantized import UnquantizedSparseMoELayer
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.utils.log import log_once
 from text_generation_server.utils.weights import (
     DefaultWeightsLoader,
-    UnquantizedWeight,
     Weights,
+    UnquantizedWeight,
 )
 
-if SYSTEM != "ipex":
+if SYSTEM == "rocm":
+    from .fused_moe_rocm import grouped_topk
+    from vllm.model_executor.layers.fused_moe import fused_topk
+elif SYSTEM != "ipex":
     from moe_kernels.fused_moe import fused_topk, grouped_topk
 
 
@@ -202,14 +210,24 @@ class SparseMoELayer(nn.Module):
             and isinstance(weights.loader.weight_class, UnquantizedWeight)
         ) or isinstance(weights.loader, HybridFP8UnquantLoader):
             cls = UnquantizedSparseMoELayer
-            # Once we wire up GPTQ-Marlin MoE:
-            # elif isinstance(weights.loader, GPTQMarlinWeightsLoader) and weights.loader.sym:
-            # cls = GPTQMarlinSparseMoELayer
+        elif isinstance(
+            weights.loader, GPTQMarlinWeightsLoader
+        ) and can_use_marlin_moe_gemm(
+            quant_method=weights.loader.quant_method,
+            quantize=weights.loader.quantize,
+            sym=weights.loader.sym,
+        ):
+            cls = GPTQMarlinSparseMoELayer
         else:
             raise ValueError(
-                f"Unsupported weights loader: {weights.loader}, sparse MoE is only supported for unquantized and GPTQ weights"
+                f"Unsupported weights loader: {type(weights.loader)}, sparse MoE is only supported for unquantized, AWQ, and GPTQ weights"
             )
 
+        log_once(
+            logger.info,
+            "Using MoE layer wih fused gemm",
+        )
+
         self.moe = cls(
             n_expert_group=n_expert_group,
             n_experts=n_experts,
@@ -234,6 +252,12 @@ class SparseMoELayer(nn.Module):
                 and isinstance(weights.loader.weight_class, UnquantizedWeight)
             )
             or isinstance(weights.loader, HybridFP8UnquantLoader)
-            # Once we wire up GPTQ-Marlin MoE:
-            # or isinstance(weights.loader, GPTQMarlinWeightsLoader)
+            or (
+                isinstance(weights.loader, GPTQMarlinWeightsLoader)
+                and can_use_marlin_moe_gemm(
+                    quant_method=weights.loader.quant_method,
+                    quantize=weights.loader.quantize,
+                    sym=weights.loader.sym,
+                )
+            )
         )
diff --git a/server/text_generation_server/layers/moe/fused_moe_rocm.py b/server/text_generation_server/layers/moe/fused_moe_rocm.py
new file mode 100644
index 00000000..68accb99
--- /dev/null
+++ b/server/text_generation_server/layers/moe/fused_moe_rocm.py
@@ -0,0 +1,52 @@
+# coding=utf-8
+# Copyright 2023, 2024 DeepSeek-AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Tuple
+
+import torch
+import torch.distributed
+
+
+# TODO: Remove the functions once moe_kernel are built for ROCM
+def grouped_topk(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    num_expert_group: int = 0,
+    topk_group: int = 0,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    scores = torch.softmax(gating_output, dim=-1)
+    num_token = scores.shape[0]
+    group_scores = (
+        scores.view(num_token, num_expert_group, -1).max(dim=-1).values
+    )  # [n, n_group]
+    group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=False)[
+        1
+    ]  # [n, top_k_group]
+    group_mask = torch.zeros_like(group_scores)  # [n, n_group]
+    group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
+    score_mask = (
+        group_mask.unsqueeze(-1)
+        .expand(num_token, num_expert_group, scores.shape[-1] // num_expert_group)
+        .reshape(num_token, -1)
+    )  # [n, e]
+    tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
+    topk_weights, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)
+
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+    return topk_weights, topk_ids
diff --git a/server/text_generation_server/layers/moe/gptq_marlin.py b/server/text_generation_server/layers/moe/gptq_marlin.py
new file mode 100644
index 00000000..3d4ca9d8
--- /dev/null
+++ b/server/text_generation_server/layers/moe/gptq_marlin.py
@@ -0,0 +1,228 @@
+from dataclasses import dataclass
+from typing import List, Optional
+
+import torch
+import torch.nn as nn
+
+from text_generation_server.utils.import_utils import SYSTEM
+from text_generation_server.utils.weights import Weights
+from text_generation_server.layers.marlin.gptq import (
+    GPTQMarlinWeight,
+    GPTQMarlinWeightsLoader,
+)
+
+if SYSTEM == "cuda":
+    from moe_kernels.fused_marlin_moe import fused_marlin_moe
+else:
+    fused_marlin_moe = None
+
+
+try:
+    major, _minor = torch.cuda.get_device_capability()
+    has_sm_8_0 = major >= 8
+except Exception:
+    has_sm_8_0 = False
+
+
+def can_use_marlin_moe_gemm(
+    *,
+    quant_method: str,
+    quantize: str,
+    sym: bool,
+):
+    return (
+        SYSTEM == "cuda"
+        and fused_marlin_moe is not None
+        and has_sm_8_0
+        and quantize in {"awq", "gptq"}
+        and quant_method in {"awq", "gptq"}
+        # We only support asymmetric quantization for AWQ.
+        and (sym or quant_method == "awq")
+    )
+
+
+@dataclass
+class GPTQMarlinMoEWeight:
+    qweight: torch.Tensor
+    qzeros: torch.Tensor
+    scales: torch.Tensor
+    g_idx: torch.Tensor
+    perm: torch.Tensor
+    is_full_k: bool
+
+
+class GPTQMarlinSparseMoELayer(nn.Module):
+    """
+    MoE layer that uses a fused GPTQ-Marlin kernel.
+    """
+
+    def __init__(
+        self,
+        *,
+        n_expert_group: Optional[int],
+        n_experts: int,
+        prefix: str,
+        renormalize: bool,
+        topk: int,
+        topk_group: Optional[int],
+        weights: Weights,
+        gate_proj_name: str = "gate_proj",
+        up_proj_name: str = "up_proj",
+        down_proj_name: str = "down_proj",
+    ):
+        super().__init__()
+
+        if not (
+            isinstance(weights.loader, GPTQMarlinWeightsLoader)
+            and can_use_marlin_moe_gemm(
+                quant_method=weights.loader.quant_method,
+                quantize=weights.loader.quantize,
+                sym=weights.loader.sym,
+            )
+        ):
+            raise ValueError(
+                f"Unsupported weights loader: {type(weights.loader)}, only GPTQMarlinWeightsLoader with AWQ and symmetric GPTQ quantization is supported"
+            )
+
+        assert (n_expert_group is None) == (
+            topk_group is None
+        ), "n_expert_group and topk_group must both be None or have some value"
+
+        self.n_expert_group = n_expert_group
+        self.topk = topk
+        self.topk_group = topk_group
+        self.renormalize = renormalize
+
+        self.gate_up_proj = _load_expert_multi_weights_col(
+            prefix=prefix,
+            n_experts=n_experts,
+            names=[gate_proj_name, up_proj_name],
+            weights=weights,
+        )
+
+        self.down_proj = _load_expert_weights_row(
+            prefix=prefix, n_experts=n_experts, name=down_proj_name, weights=weights
+        )
+
+        self.bits = weights.loader.bits
+
+    def forward(self, x: torch.Tensor, *, gating_output: torch.Tensor) -> torch.Tensor:
+        return fused_marlin_moe(
+            hidden_states=x,
+            w1=self.gate_up_proj.qweight,
+            w2=self.down_proj.qweight,
+            w1_scale=self.gate_up_proj.scales,
+            w2_scale=self.down_proj.scales,
+            w1_zeros=(
+                self.gate_up_proj.qzeros
+                if self.gate_up_proj.qzeros.numel() > 0
+                else None
+            ),
+            w2_zeros=(
+                self.down_proj.qzeros if self.down_proj.qzeros.numel() > 0 else None
+            ),
+            g_idx1=self.gate_up_proj.g_idx,
+            g_idx2=self.down_proj.g_idx,
+            sort_indices1=self.gate_up_proj.perm,
+            sort_indices2=self.down_proj.perm,
+            is_k_full=self.gate_up_proj.is_full_k or self.down_proj.is_full_k,
+            gating_output=gating_output,
+            topk=self.topk,
+            renormalize=self.renormalize,
+            use_grouped_topk=self.n_expert_group is not None,
+            num_expert_group=self.n_expert_group,
+            topk_group=self.topk_group,
+            num_bits=self.bits,
+        )
+
+
+def _load_expert_multi_weights_col(
+    *,
+    prefix: str,
+    n_experts: int,
+    names: List[str],
+    weights: Weights,
+) -> GPTQMarlinMoEWeight:
+    moe_weight = None
+    for i in range(n_experts):
+        weight = weights.get_multi_weights_col(
+            [f"{prefix}.{i}.{name}" for name in names], 0
+        )
+        assert isinstance(weight, GPTQMarlinWeight)
+        moe_weight = _pack_weight(
+            n_experts=n_experts, expert=i, weight=weight, moe_weight=moe_weight
+        )
+    assert moe_weight is not None
+    return moe_weight
+
+
+def _load_expert_weights_row(
+    *,
+    prefix: str,
+    n_experts: int,
+    name: str,
+    weights: Weights,
+) -> GPTQMarlinMoEWeight:
+    moe_weight = None
+    for i in range(n_experts):
+        weight = weights.get_weights_row(
+            f"{prefix}.{i}.{name}",
+        )
+        assert isinstance(weight, GPTQMarlinWeight)
+        moe_weight = _pack_weight(
+            n_experts=n_experts, expert=i, weight=weight, moe_weight=moe_weight
+        )
+    assert moe_weight is not None
+    return moe_weight
+
+
+def _pack_weight(
+    *,
+    n_experts: int,
+    expert: int,
+    moe_weight: Optional[GPTQMarlinMoEWeight],
+    weight: GPTQMarlinWeight,
+) -> GPTQMarlinMoEWeight:
+    if moe_weight is None:
+        qweight = torch.empty(
+            (n_experts,) + weight.qweight.shape,
+            dtype=weight.qweight.dtype,
+            device=weight.qweight.device,
+        )
+        qzeros = torch.empty(
+            (n_experts,) + weight.qzeros.shape,
+            dtype=weight.qzeros.dtype,
+            device=weight.qzeros.device,
+        )
+        scales = torch.empty(
+            (n_experts,) + weight.scales.shape,
+            dtype=weight.scales.dtype,
+            device=weight.scales.device,
+        )
+        g_idx = torch.empty(
+            (n_experts,) + weight.g_idx.shape,
+            dtype=weight.g_idx.dtype,
+            device=weight.g_idx.device,
+        )
+        perm = torch.empty(
+            (n_experts,) + weight.perm.shape,
+            dtype=weight.perm.dtype,
+            device=weight.perm.device,
+        )
+
+        moe_weight = GPTQMarlinMoEWeight(
+            qweight=qweight,
+            qzeros=qzeros,
+            scales=scales,
+            g_idx=g_idx,
+            perm=perm,
+            is_full_k=weight.is_full_k,
+        )
+
+    moe_weight.qweight[expert] = weight.qweight
+    moe_weight.qzeros[expert] = weight.qzeros
+    moe_weight.scales[expert] = weight.scales
+    moe_weight.g_idx[expert] = weight.g_idx
+    moe_weight.perm[expert] = weight.perm
+
+    return moe_weight
diff --git a/server/text_generation_server/layers/moe/unquantized.py b/server/text_generation_server/layers/moe/unquantized.py
index 8f1d9b3f..d9d62c0e 100644
--- a/server/text_generation_server/layers/moe/unquantized.py
+++ b/server/text_generation_server/layers/moe/unquantized.py
@@ -6,7 +6,9 @@ import torch.nn as nn
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.utils.weights import UnquantizedWeight, Weights
 
-if SYSTEM != "ipex":
+if SYSTEM == "rocm":
+    from vllm.model_executor.layers.fused_moe import fused_moe
+elif SYSTEM != "ipex":
     from moe_kernels.fused_moe import fused_moe
 
 
@@ -52,6 +54,17 @@ class UnquantizedSparseMoELayer(nn.Module):
         )
 
     def forward(self, x: torch.Tensor, *, gating_output: torch.Tensor) -> torch.Tensor:
+        if SYSTEM == "rocm":
+            return fused_moe(
+                x,
+                self.gate_up_proj,
+                self.down_proj,
+                gating_output,
+                self.topk,
+                renormalize=self.renormalize,
+                inplace=True,
+            )
+
         return fused_moe(
             x,
             w1=self.gate_up_proj,
diff --git a/server/text_generation_server/layers/rotary.py b/server/text_generation_server/layers/rotary.py
index fc4a59b9..a2076bb2 100644
--- a/server/text_generation_server/layers/rotary.py
+++ b/server/text_generation_server/layers/rotary.py
@@ -166,6 +166,20 @@ class PositionRotaryEmbedding(nn.Module):
                         1 + math.log(scale) / math.log(original_max_position_embeddings)
                     )
 
+                # if short_mscale and long_mscale are provided we need to scale the freqs
+                # using the Phi3LongRoPEScaledRotaryEmbedding
+                if ("short_mscale" in rope_scaling) and ("long_mscale" in rope_scaling):
+                    short_mscale = rope_scaling["short_mscale"]
+                    long_mscale = rope_scaling["long_mscale"]
+                    return Phi3LongRoPEScaledRotaryEmbedding(
+                        short_inv_freq=short_inv_freq,
+                        long_inv_freq=long_inv_freq,
+                        max_position_embeddings=config.max_position_embeddings,
+                        short_mscale=short_mscale,
+                        long_mscale=long_mscale,
+                        original_max_position_embeddings=original_max_position_embeddings,
+                    )
+
                 return SuRotaryEmbedding(
                     short_inv_freq=short_inv_freq,
                     long_inv_freq=long_inv_freq,
@@ -287,6 +301,7 @@ class SuRotaryEmbedding(PositionRotaryEmbedding):
         # or if we're on a new device (possibly due to tracing for instance)
         if (
             seqlen > self._seq_len_cached
+            or self._cos_cached is None
             or self._cos_cached.device != device
             or self._cos_cached.dtype != dtype
         ):
@@ -308,6 +323,63 @@ class SuRotaryEmbedding(PositionRotaryEmbedding):
             self._sin_cached = (torch.sin(freqs) * self.scaling_factor).to(dtype)
 
 
+class Phi3LongRoPEScaledRotaryEmbedding(PositionRotaryEmbedding):
+    def __init__(
+        self,
+        short_inv_freq: torch.Tensor,
+        long_inv_freq: torch.Tensor,
+        max_position_embeddings: int,
+        short_mscale: float,
+        long_mscale: float,
+        original_max_position_embeddings: int,
+    ):
+        super(PositionRotaryEmbedding, self).__init__()
+        self.short_inv_freq = short_inv_freq
+        self.long_inv_freq = long_inv_freq
+        self.max_position_embeddings = max_position_embeddings
+        self.short_mscale = short_mscale
+        self.long_mscale = long_mscale
+        self.original_max_position_embeddings = original_max_position_embeddings
+
+        # cache
+        self._seq_len_cached = 0
+        self._cos_cached = None
+        self._sin_cached = None
+        self._cos_k_cached = None
+        self._sin_k_cached = None
+        self.dynamic_args = None
+
+    def _update_cos_sin_cache(self, dtype, device, seqlen):
+        if (
+            seqlen > self._seq_len_cached
+            or self._cos_cached is None
+            or self._cos_cached.device != device
+            or self._cos_cached.dtype != dtype
+        ):
+            self._seq_len_cached = seqlen
+            t = torch.arange(seqlen, device=device, dtype=self.short_inv_freq.dtype)
+
+            short_freqs = torch.outer(
+                t[: self.original_max_position_embeddings],
+                self.short_inv_freq.to(device=t.device),
+            )
+
+            long_freqs = torch.outer(
+                t[self.original_max_position_embeddings :],
+                self.long_inv_freq.to(device=t.device),
+            )
+
+            short_freqs = short_freqs * self.short_mscale
+            long_freqs = long_freqs * self.long_mscale
+
+            freqs = torch.empty((seqlen, short_freqs.shape[1]), device=device)
+            freqs[: self.original_max_position_embeddings] = short_freqs
+            freqs[self.original_max_position_embeddings :] = long_freqs
+
+            self._cos_cached = torch.cos(freqs).to(dtype)
+            self._sin_cached = torch.sin(freqs).to(dtype)
+
+
 class DynamicPositionRotaryEmbedding(PositionRotaryEmbedding):
     def __init__(self, dim, max_position_embeddings, base, device, scaling_factor):
         inv_freq = _create_inv_freq(dim, base, device)
@@ -467,7 +539,6 @@ def apply_llama3_scaling(
         elif wavelen > low_freq_wavelen:
             new_freqs.append(freq / scaling_factor)
         else:
-
             assert low_freq_wavelen != high_freq_wavelen
             smooth = (original_max_position_embeddings / wavelen - low_freq_factor) / (
                 high_freq_factor - low_freq_factor
diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py
index e5e5aabb..17eed976 100644
--- a/server/text_generation_server/models/__init__.py
+++ b/server/text_generation_server/models/__init__.py
@@ -32,6 +32,9 @@ from text_generation_server.models.custom_modeling.phi_modeling import (
     PhiConfig,
     PhiForCausalLM,
 )
+from text_generation_server.models.custom_modeling.flash_phi_moe_modeling import (
+    PhiMoEConfig,
+)
 from text_generation_server.models.custom_modeling.t5_modeling import (
     T5ForConditionalGeneration,
 )
@@ -73,6 +76,7 @@ FLASH_ATTENTION = True
 try:
     from text_generation_server.models.flash_causal_lm import FlashCausalLM
     from text_generation_server.models.vlm_causal_lm import VlmCausalLM
+    from text_generation_server.models.mllama_causal_lm import MllamaCausalLM
     from text_generation_server.models.custom_modeling.flash_deepseek_v2_modeling import (
         FlashDeepseekV2ForCausalLM,
         DeepseekV2Config,
@@ -109,7 +113,11 @@ try:
     from text_generation_server.models.custom_modeling.flash_phi_modeling import (
         FlashPhiForCausalLM,
     )
-    from text_generation_server.models.idefics import IDEFICSSharded
+    from text_generation_server.models.idefics_causal_lm import IdeficsCausalLM
+    from text_generation_server.models.mllama_causal_lm import MllamaCausalLMBatch
+    from text_generation_server.models.custom_modeling.mllama import (
+        MllamaForConditionalGeneration,
+    )
     from text_generation_server.models.custom_modeling.llava_next import (
         LlavaNextForConditionalGeneration,
     )
@@ -146,7 +154,7 @@ except ImportError as e:
 
 if FLASH_ATTENTION:
     __all__.append(FlashCausalLM)
-    __all__.append(IDEFICSSharded)
+    __all__.append(IdeficsCausalLM)
 
 MAMBA_AVAILABLE = True
 try:
@@ -237,6 +245,11 @@ class ModelType(enum.Enum):
         "name": "Phi",
         "url": "https://huggingface.co/microsoft/phi-1_5",
     }
+    PHI_MOE = {
+        "type": "phimoe",
+        "name": "PhiMoe",
+        "url": "https://huggingface.co/microsoft/Phi-3.5-MoE-instruct",
+    }
     BAICHUAN = {
         "type": "baichuan",
         "name": "Baichuan",
@@ -308,6 +321,12 @@ class ModelType(enum.Enum):
         "url": "https://huggingface.co/HuggingFaceM4/idefics-9b",
         "multimodal": True,
     }
+    MLLAMA = {
+        "type": "mllama",
+        "name": "Mllama",
+        "url": "https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct",
+        "multimodal": True,
+    }
 
 
 __GLOBALS = locals()
@@ -323,6 +342,7 @@ def get_model(
     quantize: Optional[str],
     speculate: Optional[int],
     dtype: Optional[str],
+    kv_cache_dtype: Optional[str],
     trust_remote_code: bool,
     max_input_tokens: int,
 ) -> Model:
@@ -384,6 +404,13 @@ def get_model(
     else:
         raise RuntimeError(f"Unknown dtype {dtype}")
 
+    if kv_cache_dtype is None:
+        kv_cache_dtype = dtype
+    elif kv_cache_dtype == "fp8_e5m2":
+        kv_cache_dtype = torch.float8_e5m2
+    else:
+        raise RuntimeError(f"Unknown kv_cache_dtype: {kv_cache_dtype}")
+
     if speculate is not None:
         set_speculate(speculate)
     else:
@@ -544,6 +571,7 @@ def get_model(
                 speculator=speculator,
                 default_dtype=torch.bfloat16,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
                 config_class=DeepseekV2Config,
@@ -598,6 +626,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
                 aliases={"transformer.wte.weight": ["lm_head.weight"]},
@@ -649,6 +678,7 @@ def get_model(
                     quantize=quantize,
                     speculator=speculator,
                     dtype=dtype,
+                    kv_cache_dtype=kv_cache_dtype,
                     trust_remote_code=trust_remote_code,
                     lora_adapter_ids=lora_adapter_ids,
                 )
@@ -684,6 +714,7 @@ def get_model(
                     quantize=quantize,
                     speculator=speculator,
                     dtype=dtype,
+                    kv_cache_dtype=kv_cache_dtype,
                     trust_remote_code=trust_remote_code,
                     lora_adapter_ids=lora_adapter_ids,
                 )
@@ -722,6 +753,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
                 config_class=GPTNeoXConfig,
@@ -755,6 +787,31 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
+                trust_remote_code=trust_remote_code,
+                lora_adapter_ids=lora_adapter_ids,
+            )
+        else:
+            return CausalLM.fallback(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    elif model_type == PHI_MOE:
+        if FLASH_ATTENTION:
+            return FlashCausalLM(
+                model_id=model_id,
+                model_class=FlashLlamaForCausalLM,
+                config_class=PhiMoEConfig,
+                revision=revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
@@ -794,6 +851,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
@@ -817,6 +875,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 # Works better for these models
                 default_dtype=torch.bfloat16,
                 trust_remote_code=trust_remote_code,
@@ -842,6 +901,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 # Works better for these models
                 default_dtype=torch.bfloat16,
                 trust_remote_code=trust_remote_code,
@@ -868,6 +928,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
@@ -892,6 +953,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 # Dbrx works better in bfloat16.
                 default_dtype=torch.bfloat16,
                 trust_remote_code=trust_remote_code,
@@ -922,6 +984,7 @@ def get_model(
                     quantize=quantize,
                     speculator=speculator,
                     dtype=dtype,
+                    kv_cache_dtype=kv_cache_dtype,
                     aliases={
                         "lm_head.weight": ["transformer.word_embeddings.weight"],
                         "transformer.word_embeddings.weight": ["lm_head.weight"],
@@ -940,6 +1003,7 @@ def get_model(
                     quantize=quantize,
                     speculator=speculator,
                     dtype=dtype,
+                    kv_cache_dtype=kv_cache_dtype,
                     aliases={
                         "lm_head.weight": ["transformer.word_embeddings.weight"],
                         "transformer.word_embeddings.weight": ["lm_head.weight"],
@@ -967,6 +1031,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
@@ -991,6 +1056,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
@@ -1015,6 +1081,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
@@ -1041,6 +1108,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
@@ -1085,7 +1153,7 @@ def get_model(
         )
     if model_type == IDEFICS:
         if FLASH_ATTENTION:
-            return IDEFICSSharded(
+            return IdeficsCausalLM(
                 model_id,
                 revision,
                 quantize=quantize,
@@ -1095,6 +1163,22 @@ def get_model(
             )
         else:
             raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
+    if model_type == MLLAMA:
+        if FLASH_ATTENTION:
+            return MllamaCausalLM(
+                model_id=model_id,
+                model_class=MllamaForConditionalGeneration,
+                batch_class=MllamaCausalLMBatch,
+                revision=revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                default_dtype=torch.bfloat16,
+                trust_remote_code=trust_remote_code,
+                lora_adapter_ids=lora_adapter_ids,
+            )
+        else:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Mllama"))
     if model_type == IDEFICS2:
         if FLASH_ATTENTION:
             return VlmCausalLM(
@@ -1104,6 +1188,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
                 # XXX: Extremely important to cap resolution in order to limit
@@ -1121,6 +1206,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 # Works better for these models
                 default_dtype=torch.bfloat16,
                 trust_remote_code=trust_remote_code,
@@ -1139,6 +1225,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 trust_remote_code=trust_remote_code,
             )
         else:
@@ -1211,6 +1298,7 @@ def get_model_with_lora_adapters(
     quantize: Optional[str],
     speculate: Optional[int],
     dtype: Optional[str],
+    kv_cache_dtype: Optional[str],
     trust_remote_code: bool,
     max_input_tokens: int,
     adapter_to_index: Dict[str, int],
@@ -1224,6 +1312,7 @@ def get_model_with_lora_adapters(
         quantize,
         speculate,
         dtype,
+        kv_cache_dtype,
         trust_remote_code,
         max_input_tokens,
     )
diff --git a/server/text_generation_server/models/causal_lm.py b/server/text_generation_server/models/causal_lm.py
index 28534d0f..ef46cb8c 100644
--- a/server/text_generation_server/models/causal_lm.py
+++ b/server/text_generation_server/models/causal_lm.py
@@ -517,14 +517,13 @@ class CausalLM(Model):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = default_dtype if dtype is None else dtype
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            device = torch.device(f"xpu:{rank}")
+            dtype = default_dtype if dtype is None else dtype
         elif SYSTEM == "ipex":
-            if hasattr(torch, "xpu") and torch.xpu.is_available():
-                device = torch.device(f"xpu:{rank}")
-                dtype = default_dtype if dtype is None else dtype
-            else:
-                device = torch.device("cpu")
-                # Float16 doesn't exist on target.
-                dtype = torch.bfloat16 if dtype is None else dtype
+            device = torch.device("cpu")
+            # Float16 doesn't exist on target.
+            dtype = torch.bfloat16 if dtype is None else dtype
         else:
             device = torch.device("cpu")
             dtype = torch.float32 if dtype is None else dtype
@@ -593,8 +592,14 @@ class CausalLM(Model):
         if speculator:
             raise RuntimeError("Speculator decoding is not enabled for AutoModel")
 
+        device_count = 0
         if torch.cuda.is_available():
             device = torch.device("cuda")
+            device_count = torch.cuda.device_count()
+            dtype = torch.float16 if dtype is None else dtype
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            device = torch.device("xpu")
+            device_count = torch.xpu.device_count()
             dtype = torch.float16 if dtype is None else dtype
         else:
             if quantize:
@@ -614,20 +619,12 @@ class CausalLM(Model):
             model_id,
             revision=revision,
             torch_dtype=dtype,
-            device_map=(
-                "auto"
-                if torch.cuda.is_available() and torch.cuda.device_count() > 1
-                else None
-            ),
+            device_map=("auto" if device_count > 1 else None),
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )
-        if (
-            torch.cuda.is_available()
-            and torch.cuda.device_count() == 1
-            and quantize != "bitsandbytes"
-        ):
-            model = model.cuda()
+        if device_count == 1 and quantize != "bitsandbytes":
+            model = model.to(device)
 
         if tokenizer.pad_token_id is None:
             if model.config.pad_token_id is not None:
diff --git a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
index 30656038..d0425fec 100644
--- a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
@@ -28,7 +28,6 @@ from typing import Optional, List, Tuple
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.utils.import_utils import SYSTEM
@@ -291,15 +290,15 @@ class FlashCohereAttention(torch.nn.Module):
 
         self.rotary_emb(query, key, cos, sin)
 
-        reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=key, value=value, slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else key,
-                kv_cache[1] if PREFILL_IN_KV_CACHE else value,
+                kv_cache.key if PREFILL_IN_KV_CACHE else key,
+                kv_cache.value if PREFILL_IN_KV_CACHE else value,
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -308,8 +307,8 @@ class FlashCohereAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
index 1137a453..b2b0cecb 100644
--- a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
@@ -28,7 +28,6 @@ if SYSTEM != "ipex":
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
     PREFILL_IN_KV_CACHE,
 )
@@ -330,15 +329,15 @@ class DbrxAttention(torch.nn.Module):
 
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
-        reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -347,8 +346,8 @@ class DbrxAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
index ac191ec3..af77af8e 100644
--- a/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
@@ -33,7 +33,6 @@ from text_generation_server.layers.attention import (
     Seqlen,
     attention,
     paged_attention,
-    reshape_and_cache,
 )
 from text_generation_server.layers.attention import PREFILL_IN_KV_CACHE
 from text_generation_server.layers.layernorm import FastRMSNorm
@@ -321,15 +320,15 @@ class DeepseekV2Attention(torch.nn.Module):
             value, (0, self.head_pad_size - self.value_head_size), value=0
         )
 
-        reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=key, value=value, slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else key,
-                kv_cache[1] if PREFILL_IN_KV_CACHE else value,
+                kv_cache.key if PREFILL_IN_KV_CACHE else key,
+                kv_cache.value if PREFILL_IN_KV_CACHE else value,
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -338,8 +337,8 @@ class DeepseekV2Attention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
@@ -390,6 +389,7 @@ class DeepseekV2MLP(nn.Module):
         if (
             SYSTEM == "rocm"
             and self.hidden_act == "silu"
+            and hidden_states.dtype == torch.float16
             and hidden_states.shape[0] == 1
             and not self.quantize
         ):
diff --git a/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
index 7a3d60c9..03b9b2a0 100644
--- a/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
@@ -28,7 +28,6 @@ from typing import Optional, List, Tuple
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.layers import (
@@ -253,15 +252,15 @@ class FlashGemma2Attention(torch.nn.Module):
 
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
-        reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -273,8 +272,8 @@ class FlashGemma2Attention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
index 4c1be6f6..f3c46901 100644
--- a/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
@@ -28,7 +28,6 @@ from typing import Optional, List, Tuple
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
     PREFILL_IN_KV_CACHE,
 )
@@ -224,15 +223,15 @@ class FlashGemmaAttention(torch.nn.Module):
 
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
-        reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -242,8 +241,8 @@ class FlashGemmaAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py
index 44c015cf..94a8898d 100644
--- a/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py
@@ -28,7 +28,6 @@ from text_generation_server.layers.attention import PREFILL_IN_KV_CACHE
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.layers import (
@@ -224,15 +223,15 @@ class FlashGPT2Attention(torch.nn.Module):
         key = key.view(-1, self.num_heads, self.head_size)
         value = value.view(-1, self.num_heads, self.head_size)
 
-        reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=key, value=value, slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else key,
-                kv_cache[1] if PREFILL_IN_KV_CACHE else value,
+                kv_cache.key if PREFILL_IN_KV_CACHE else key,
+                kv_cache.value if PREFILL_IN_KV_CACHE else value,
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -241,8 +240,8 @@ class FlashGPT2Attention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py
index aca97004..f0a1270e 100644
--- a/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py
@@ -28,7 +28,6 @@ from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.layers import (
@@ -186,15 +185,15 @@ class FlashGPTJAttention(torch.nn.Module):
         else:
             self.rotary_emb(query, key, cos, sin)
 
-        reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=key, value=value, slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else key,
-                kv_cache[1] if PREFILL_IN_KV_CACHE else value,
+                kv_cache.key if PREFILL_IN_KV_CACHE else key,
+                kv_cache.value if PREFILL_IN_KV_CACHE else value,
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -203,8 +202,8 @@ class FlashGPTJAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
index 758e39aa..fbe45d79 100644
--- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
@@ -19,7 +19,7 @@
 # limitations under the License.
 
 from contextlib import contextmanager
-from typing import List, Optional, Tuple
+from typing import List, Optional, Tuple, Type
 
 import torch
 import torch.distributed
@@ -27,12 +27,12 @@ import torch.distributed
 from torch import nn
 from transformers.activations import ACT2FN
 
-from text_generation_server.layers.attention import PREFILL_IN_KV_CACHE
+from text_generation_server.layers.attention import PREFILL_IN_KV_CACHE, KVCache
+from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.layers import (
@@ -46,12 +46,19 @@ from text_generation_server.layers import (
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.layernorm import (
     FastRMSNorm,
+    FastLayerNorm,
+)
+from text_generation_server.layers import (
+    FastLinear,
 )
 from text_generation_server.utils.weights import (
     Weights,
 )
 from text_generation_server.layers.fp8 import HybridFP8UnquantLoader
 
+if SYSTEM != "ipex":
+    pass
+
 if SYSTEM == "rocm":
     try:
         from vllm import _custom_C
@@ -194,7 +201,7 @@ class FlashLlamaAttention(torch.nn.Module):
         cos,
         sin,
         cu_seqlen_prefill,
-        kv_cache,
+        kv_cache: KVCache,
         block_tables,
         slots,
         seqlen,
@@ -214,15 +221,15 @@ class FlashLlamaAttention(torch.nn.Module):
 
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
-        reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -231,8 +238,8 @@ class FlashLlamaAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
@@ -245,6 +252,42 @@ class FlashLlamaAttention(torch.nn.Module):
         )
 
 
+class Phi3MoE(nn.Module):
+    def __init__(
+        self, prefix: str, config, moe_layer_cls: Type[MoELayer], weights: Weights
+    ):
+        super().__init__()
+
+        # gating
+        self.gate = FastLinear.load(config, f"{prefix}.gate", weights, bias=False)
+
+        self.moe = moe_layer_cls(
+            prefix=f"{prefix}.experts",
+            n_experts=config.num_local_experts,
+            n_expert_group=None,
+            renormalize=True,
+            topk=config.num_experts_per_tok,
+            topk_group=None,
+            weights=weights,
+            gate_proj_name="w1",
+            up_proj_name="w3",
+            down_proj_name="w2",
+        )
+
+        self.process_group = weights.process_group
+
+    def forward(self, x, adapter_data) -> torch.Tensor:
+        # router_logits: (num_tokens, n_experts)
+        router_logits = self.gate(x)
+        out = self.moe(x, gating_output=router_logits)
+
+        # Reduce sum
+        if self.process_group.size() > 1:
+            torch.distributed.all_reduce(out, group=self.process_group)
+
+        return out.view(*x.shape)
+
+
 class LlamaMLP(nn.Module):
     def __init__(self, prefix, config, weights, index):
         super().__init__()
@@ -316,12 +359,17 @@ class LlamaMLP(nn.Module):
         # TODO: This is a hotfix to be removed & properly refactored.
         self.quantize = config.quantize
 
+        self.hidden_size = config.hidden_size
+
     def forward(self, hidden_states, adapter_data):
         if (
             SYSTEM == "rocm"
             and self.hidden_act == "silu"
+            and hidden_states.dtype == torch.float16
             and hidden_states.shape[0] == 1
             and not self.quantize
+            and self.hidden_size
+            != 16384  # TODO: Temporary workaround for `LLMM_Silu` kernel not working with LLama3.1 405B; needs refactoring once fixed.
         ):
             out = torch.empty(
                 hidden_states.shape[0],
@@ -353,18 +401,40 @@ class FlashLlamaLayer(nn.Module):
                 weights=weights,
             )
 
-        self.mlp = LlamaMLP(
-            prefix=f"{prefix}.mlp", config=config, weights=weights, index=index
-        )
-
-        self.input_layernorm = FastRMSNorm.load(
-            prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
-        )
-        self.post_attention_layernorm = FastRMSNorm.load(
-            prefix=f"{prefix}.post_attention_layernorm",
-            weights=weights,
-            eps=config.rms_norm_eps,
-        )
+        if config.model_type == "phimoe":
+            moe_layer_cls = (
+                SparseMoELayer
+                if SparseMoELayer.is_supported(weights)
+                else DenseMoELayer
+            )
+            self.dense = Phi3MoE(
+                f"{prefix}.block_sparse_moe", config, moe_layer_cls, weights
+            )
+            # with moe the layernorms are are not rmsnorms and they have bias
+            self.input_layernorm = FastLayerNorm.load(
+                prefix=f"{prefix}.input_layernorm",
+                weights=weights,
+                eps=config.rms_norm_eps,
+            )
+            self.post_attention_layernorm = FastLayerNorm.load(
+                prefix=f"{prefix}.post_attention_layernorm",
+                weights=weights,
+                eps=config.rms_norm_eps,
+            )
+        else:
+            self.dense = LlamaMLP(
+                prefix=f"{prefix}.mlp", config=config, weights=weights, index=index
+            )
+            self.input_layernorm = FastRMSNorm.load(
+                prefix=f"{prefix}.input_layernorm",
+                weights=weights,
+                eps=config.rms_norm_eps,
+            )
+            self.post_attention_layernorm = FastRMSNorm.load(
+                prefix=f"{prefix}.post_attention_layernorm",
+                weights=weights,
+                eps=config.rms_norm_eps,
+            )
 
     def forward(
         self,
@@ -379,6 +449,7 @@ class FlashLlamaLayer(nn.Module):
         seqlen,
         max_s,
         adapter_data,
+        cross_attention_states,
     ):
         normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
 
@@ -401,7 +472,7 @@ class FlashLlamaLayer(nn.Module):
             attn_output, res
         )
 
-        mlp_output = self.mlp(normed_attn_res_output, adapter_data)
+        mlp_output = self.dense(normed_attn_res_output, adapter_data)
 
         return mlp_output, attn_res
 
@@ -416,6 +487,7 @@ class FlashLlamaModel(torch.nn.Module):
 
         # Skip fp8 quant for first and last layers
         self.layers = nn.ModuleList()
+        self.cross_attention_layers = getattr(config, "cross_attention_layers", [])
         with no_fp8(weights):
             self.layers.append(
                 FlashLlamaLayer(
@@ -428,22 +500,38 @@ class FlashLlamaModel(torch.nn.Module):
                 )
             )
 
-        self.layers.extend(
-            [
-                FlashLlamaLayer(
-                    index=layer_id,
-                    prefix=(
-                        f"model.layers.{layer_id}"
-                        if not prefix
-                        else f"{prefix}.model.layers.{layer_id}"
-                    ),
-                    config=config,
-                    weights=weights,
+        # Skip first and last layers
+        for layer_id in range(1, config.num_hidden_layers - 1):
+            if layer_id in self.cross_attention_layers:
+                from text_generation_server.models.custom_modeling.mllama import (
+                    FlashLlamaCrossLayer,
+                )
+
+                self.layers.append(
+                    FlashLlamaCrossLayer(
+                        index=layer_id,
+                        prefix=(
+                            f"model.layers.{layer_id}"
+                            if not prefix
+                            else f"{prefix}.model.layers.{layer_id}"
+                        ),
+                        config=config,
+                        weights=weights,
+                    )
+                )
+            else:
+                self.layers.append(
+                    FlashLlamaLayer(
+                        index=layer_id,
+                        prefix=(
+                            f"model.layers.{layer_id}"
+                            if not prefix
+                            else f"{prefix}.model.layers.{layer_id}"
+                        ),
+                        config=config,
+                        weights=weights,
+                    )
                 )
-                # Skip first and last layers
-                for layer_id in range(1, config.num_hidden_layers - 1)
-            ]
-        )
 
         with no_fp8(weights):
             last_layer_id = config.num_hidden_layers - 1
@@ -485,6 +573,7 @@ class FlashLlamaModel(torch.nn.Module):
         true_max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
         adapter_data,
+        cross_attention_states=None,
     ) -> torch.Tensor:
         hidden_states = inputs_embeds
 
@@ -508,6 +597,7 @@ class FlashLlamaModel(torch.nn.Module):
                 seqlen,
                 max_s,
                 adapter_data,
+                cross_attention_states,
             )
 
         hidden_states, _ = self.norm(hidden_states, residual)
@@ -554,6 +644,7 @@ class FlashLlamaForCausalLM(torch.nn.Module):
         prefill_cache_indices: Optional[torch.Tensor] = None,
         lm_head_indices: Optional[torch.Tensor] = None,
         adapter_data: Optional[torch.Tensor] = None,
+        cross_attention_states=None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         inputs_embeds = self.embed_tokens(input_ids)
         hidden_states = self.model(
@@ -568,6 +659,7 @@ class FlashLlamaForCausalLM(torch.nn.Module):
             true_max_s=max_s,
             prefill_cache_indices=prefill_cache_indices,
             adapter_data=adapter_data,
+            cross_attention_states=cross_attention_states,
         )
         if lm_head_indices is not None:
             hidden_states = hidden_states[lm_head_indices]
diff --git a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py
index 3e16d371..8974035e 100644
--- a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py
@@ -30,7 +30,6 @@ from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.layers import (
@@ -210,17 +209,15 @@ class MistralAttention(torch.nn.Module):
         else:
             kv_to_cache = kv
 
-        reshape_and_cache(
-            kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
-        )
+        kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv_to_cache[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv_to_cache[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv_to_cache[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv_to_cache[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -230,8 +227,8 @@ class MistralAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
@@ -303,6 +300,7 @@ class MistralMLP(nn.Module):
         if (
             SYSTEM == "rocm"
             and self.hidden_act == "silu"
+            and hidden_states.dtype == torch.float16
             and hidden_states.shape[0] == 1
             and not self.quantize
         ):
diff --git a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py
index 5836d30a..e7bc8320 100644
--- a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py
@@ -37,7 +37,6 @@ from text_generation_server.layers.attention import (
     Seqlen,
     attention,
     paged_attention,
-    reshape_and_cache,
 )
 from text_generation_server.layers.attention import PREFILL_IN_KV_CACHE
 from text_generation_server.layers.layernorm import FastRMSNorm
@@ -258,17 +257,15 @@ class MixtralAttention(torch.nn.Module):
         else:
             kv_to_cache = kv
 
-        reshape_and_cache(
-            kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
-        )
+        kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv_to_cache[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv_to_cache[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv_to_cache[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv_to_cache[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -278,8 +275,8 @@ class MixtralAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
index ad4e382f..bcbea442 100644
--- a/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
@@ -29,7 +29,6 @@ from typing import Optional, List, Tuple
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.layers import (
@@ -165,15 +164,15 @@ class FlashNeoxAttention(torch.nn.Module):
         qkv[:, 0] = torch.cat((query_rot, query_pass), dim=-1)
         qkv[:, 1] = torch.cat((key_rot, key_pass), dim=-1)
 
-        reshape_and_cache(qkv[:, 1], qkv[:, 2], kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=qkv[:, 1], value=qkv[:, 2], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 qkv[:, 0],
-                kv_cache[0] if PREFILL_IN_KV_CACHE else qkv[:, 1],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else qkv[:, 2],
+                kv_cache.key if PREFILL_IN_KV_CACHE else qkv[:, 1],
+                kv_cache.value if PREFILL_IN_KV_CACHE else qkv[:, 2],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -182,8 +181,8 @@ class FlashNeoxAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 qkv[:, 0],
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py b/server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py
index d044b492..0024f2bb 100644
--- a/server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py
@@ -48,7 +48,7 @@ class PaliGemmaForConditionalGeneration(nn.Module):
             bias=True,
         )
 
-        self.vocab_size = config.vocab_size
+        self.vocab_size = config.text_config.vocab_size
         self.config = config
 
         text_config = config.text_config
diff --git a/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py b/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py
index 2a0dc606..cb7b6ee2 100644
--- a/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py
@@ -9,7 +9,6 @@ from typing import Optional, List, Tuple
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.layers import (
@@ -188,14 +187,14 @@ class FlashPhiAttention(torch.nn.Module):
         )
 
         # Reshape key and value and cache
-        reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -204,8 +203,8 @@ class FlashPhiAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_phi_moe_modeling.py b/server/text_generation_server/models/custom_modeling/flash_phi_moe_modeling.py
new file mode 100644
index 00000000..bb585cc4
--- /dev/null
+++ b/server/text_generation_server/models/custom_modeling/flash_phi_moe_modeling.py
@@ -0,0 +1,254 @@
+# coding=utf-8
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""PyTorch Phi-MoE model."""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+PHIMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "microsoft/Phi-3.5-MoE-instruct": "https://huggingface.co/microsoft/Phi-3.5-MoE-instruct/resolve/main/config.json",
+}
+
+
+class PhiMoEConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`PhiMoEModel`]. It is used to instantiate a Phi-MoE
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the
+    [microsoft/Phi-3.5-MoE-instruct](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32064):
+            Vocabulary size of the PhiMoE model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`PhiMoEModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 6400):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`dict`, *optional*):
+            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
+            contain the following keys: `type`, `short_factor`, `long_factor`, `short_mscale`, `long_mscale` and
+            `original_max_position_embeddings`. The `type` must be `longrope`, the `short_mscale` and `long_scale` must
+            be numbers, the `short_factor` and `long_factor` must be lists of numbers with the same length as half of
+            the attention head size and the `original_max_position_embeddings` must be an integer.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `262144`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts to root per-token, can be also interpreted as the `top-p` routing
+            parameter
+        num_local_experts (`int`, *optional*, defaults to 16):
+            Number of experts per Sparse MLP layer.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not the router logits should be returned by the model. Enabeling this will also
+            allow the model to output the auxiliary loss. See [here]() for more details
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.0):
+            The aux loss factor for the total loss.
+        router_jitter_noise (`float`, *optional*, defaults to 0.01):
+            Amount of noise to add to the router.
+
+    ```python
+    >>> from transformers import PhiMoEModel, PhiMoEConfig
+
+    >>> # Initializing a Phi-3 style configuration
+    >>> configuration = PhiMoEConfig.from_pretrained("microsoft/Phi-3.5-MoE-instruct")
+
+    >>> # Initializing a model from the configuration
+    >>> model = PhiMoEModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "phimoe"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32064,
+        hidden_size=4096,
+        intermediate_size=6400,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=1e6,
+        rope_scaling=None,
+        sliding_window=None,
+        attention_dropout=0.0,
+        num_experts_per_tok=2,
+        num_local_experts=16,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        router_jitter_noise=0.01,
+        input_jitter_noise=0.0,
+        attention_bias=False,
+        lm_head_bias=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+        self.attention_bias = attention_bias
+        self.lm_head_bias = lm_head_bias
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.router_jitter_noise = router_jitter_noise
+        self.input_jitter_noise = input_jitter_noise
+
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 6:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor`, `long_factor`, "
+                f"`short_mscale`, `long_mscale` and `original_max_position_embeddings`, got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
+        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
+        rope_scaling_short_mscale = self.rope_scaling.get("short_mscale", None)
+        rope_scaling_long_mscale = self.rope_scaling.get("long_mscale", None)
+        original_max_position_embeddings = self.rope_scaling.get(
+            "original_max_position_embeddings", None
+        )
+        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}"
+            )
+        if not (
+            isinstance(rope_scaling_short_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
+            )
+        if (
+            not len(rope_scaling_short_factor)
+            == self.hidden_size // self.num_attention_heads // 2
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
+            )
+        if not (
+            isinstance(rope_scaling_long_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
+            )
+        if (
+            not len(rope_scaling_long_factor)
+            == self.hidden_size // self.num_attention_heads // 2
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
+            )
+        if not isinstance(rope_scaling_short_mscale, (int, float)):
+            raise ValueError(
+                f"`rope_scaling`'s short_mscale field must be a number, got {rope_scaling_short_mscale}"
+            )
+        if not isinstance(rope_scaling_long_mscale, (int, float)):
+            raise ValueError(
+                f"`rope_scaling`'s long_mscale field must be a number, got {rope_scaling_long_mscale}"
+            )
+        if not isinstance(original_max_position_embeddings, int):
+            raise ValueError(
+                f"`rope_scaling`'s original_max_position_embeddings field must be an integer, got {original_max_position_embeddings}"
+            )
diff --git a/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py
index 02c788d3..8185885f 100644
--- a/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py
@@ -8,7 +8,6 @@ from typing import Optional, List, Tuple
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.layers import (
@@ -128,17 +127,15 @@ class Qwen2Attention(torch.nn.Module):
         else:
             kv_to_cache = kv
 
-        reshape_and_cache(
-            kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
-        )
+        kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv_to_cache[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv_to_cache[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv_to_cache[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv_to_cache[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -148,8 +145,8 @@ class Qwen2Attention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
index 6671d85e..dac8ecf9 100644
--- a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
@@ -18,7 +18,6 @@ from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.attention import (
     attention,
     paged_attention,
-    reshape_and_cache,
     Seqlen,
 )
 
@@ -200,15 +199,15 @@ class FlashRWAttention(torch.nn.Module):
         # Inplace rotary
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
-        reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
+        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -217,8 +216,8 @@ class FlashRWAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
@@ -312,12 +311,8 @@ class FlashRWLargeAttention(torch.nn.Module):
         # Inplace rotary
         self.rotary_emb(query, torch.select(kv, dim=2, index=0), cos, sin)
 
-        reshape_and_cache(
-            kv[:, :, 0].contiguous(),
-            kv[:, :, 1].contiguous(),
-            kv_cache[0],
-            kv_cache[1],
-            slots,
+        kv_cache.store(
+            key=kv[:, :, 0].contiguous(), value=kv[:, :, 1].contiguous(), slots=slots
         )
 
         # Prefill
@@ -325,8 +320,8 @@ class FlashRWLargeAttention(torch.nn.Module):
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv[:, :, 0].contiguous(),
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv[:, :, 1].contiguous(),
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv[:, :, 0].contiguous(),
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv[:, :, 1].contiguous(),
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -335,8 +330,8 @@ class FlashRWLargeAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
index 43eb9687..5972d436 100644
--- a/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
@@ -8,7 +8,6 @@ from typing import Optional, List, Tuple
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.layers import (
@@ -284,17 +283,15 @@ class FlashMQAttention(torch.nn.Module):
         query = query.view(-1, self.num_heads, self.head_size)
         key_value = key_value.view(-1, 2, 1, self.head_size)
 
-        reshape_and_cache(
-            key_value[:, 0], key_value[:, 1], kv_cache[0], kv_cache[1], slots
-        )
+        kv_cache.store(key=key_value[:, 0], value=key_value[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else key_value[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else key_value[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else key_value[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else key_value[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -303,8 +300,8 @@ class FlashMQAttention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
index 4975cf22..037238b8 100644
--- a/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
@@ -29,7 +29,6 @@ from typing import Optional, List, Tuple
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
-    reshape_and_cache,
     Seqlen,
 )
 from text_generation_server.layers import (
@@ -233,17 +232,15 @@ class Starcoder2Attention(torch.nn.Module):
         else:
             kv_to_cache = kv
 
-        reshape_and_cache(
-            kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
-        )
+        kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
 
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
             attn_output = attention(
                 query,
-                kv_cache[0] if PREFILL_IN_KV_CACHE else kv_to_cache[:, 0],
-                kv_cache[1] if PREFILL_IN_KV_CACHE else kv_to_cache[:, 1],
+                kv_cache.key if PREFILL_IN_KV_CACHE else kv_to_cache[:, 0],
+                kv_cache.value if PREFILL_IN_KV_CACHE else kv_to_cache[:, 1],
                 seqlen,
                 block_tables,
                 self.softmax_scale,
@@ -253,8 +250,8 @@ class Starcoder2Attention(torch.nn.Module):
         else:
             attn_output = paged_attention(
                 query,
-                kv_cache[0],
-                kv_cache[1],
+                kv_cache.key,
+                kv_cache.value,
                 self.kv_head_mapping,
                 self.softmax_scale,
                 block_tables,
diff --git a/server/text_generation_server/models/custom_modeling/mllama.py b/server/text_generation_server/models/custom_modeling/mllama.py
new file mode 100644
index 00000000..6e091a74
--- /dev/null
+++ b/server/text_generation_server/models/custom_modeling/mllama.py
@@ -0,0 +1,1031 @@
+# coding=utf-8
+# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Mllama model."""
+
+from typing import Optional, Tuple, List
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from text_generation_server.utils.import_utils import SYSTEM
+
+if SYSTEM == "ipex":
+    import intel_extension_for_pytorch as ipex
+else:
+    import flash_attn_2_cuda
+
+from transformers.activations import ACT2FN
+import torch.nn.functional as F
+
+from text_generation_server.layers import (
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+    FastLinear,
+)
+from text_generation_server.layers.attention import (
+    Seqlen,
+)
+from text_generation_server.models.custom_modeling.flash_llama_modeling import (
+    FlashLlamaForCausalLM,
+)
+
+
+def _prepare_aspect_ratio_attention_mask(
+    aspect_ratio_mask: torch.Tensor,
+    num_patches: int,
+    target_length: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    # Expand aspect ratio mask to target_length
+    batch_size, max_num_tiles = aspect_ratio_mask.shape
+    attention_mask = aspect_ratio_mask.view(batch_size, max_num_tiles, 1, 1).to(dtype)
+    attention_mask = attention_mask.repeat(1, 1, target_length, 1)
+
+    # Mask padding patches
+    pad_patches = target_length - num_patches
+    attention_mask[:, :, -pad_patches:] = 0
+
+    # Invert the mask (0 -> 1, 1 -> 0)
+    attention_mask = 1 - attention_mask
+
+    # Reshape to 2D and create 4D attention mask
+    # (batch_size, 1, max_num_tiles * target_length, max_num_tiles * target_length)
+    attention_mask = attention_mask.reshape(
+        batch_size, max_num_tiles * target_length, 1
+    )
+    attention_mask = (
+        attention_mask @ attention_mask.transpose(-1, -2) * torch.finfo(dtype).min
+    )
+    attention_mask = attention_mask.unsqueeze(1)
+
+    return attention_mask
+
+
+# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
+def _prepare_4d_causal_attention_mask_with_cache_position(
+    attention_mask: torch.Tensor,
+    sequence_length: int,
+    target_length: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    min_dtype: float,
+    cache_position: torch.Tensor,
+    batch_size: int,
+):
+    """
+    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+    `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+    Args:
+        attention_mask (`torch.Tensor`):
+            A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+        sequence_length (`int`):
+            The sequence length being processed.
+        target_length (`int`):
+            The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+        dtype (`torch.dtype`):
+            The dtype to use for the 4D attention mask.
+        device (`torch.device`):
+            The device to plcae the 4D attention mask on.
+        min_dtype (`float`):
+            The minimum value representable with the dtype `dtype`.
+        cache_position (`torch.Tensor`):
+            Indices depicting the position of the input sequence tokens in the sequence.
+        batch_size (`torch.Tensor`):
+            Batch size.
+    """
+    if attention_mask is not None and attention_mask.dim() == 4:
+        # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+        causal_mask = attention_mask
+    else:
+        causal_mask = torch.full(
+            (sequence_length, target_length),
+            fill_value=min_dtype,
+            dtype=dtype,
+            device=device,
+        )
+        if sequence_length != 1:
+            causal_mask = torch.triu(causal_mask, diagonal=1)
+        causal_mask *= torch.arange(
+            target_length, device=device
+        ) > cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+        if attention_mask is not None:
+            causal_mask = (
+                causal_mask.clone()
+            )  # copy to contiguous memory for in-place edit
+            mask_length = attention_mask.shape[-1]
+            padding_mask = (
+                causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+            )
+            padding_mask = padding_mask == 0
+            causal_mask[:, :, :, :mask_length] = causal_mask[
+                :, :, :, :mask_length
+            ].masked_fill(padding_mask, min_dtype)
+
+    return causal_mask
+
+
+def _prepare_cross_attention_mask(
+    cross_attention_mask: torch.Tensor,
+    num_vision_tokens: int,
+    dtype: str,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    # reshape so it can be used by attn module
+    batch_size, text_total_length, *_ = cross_attention_mask.shape
+    cross_attention_mask = cross_attention_mask.repeat_interleave(
+        num_vision_tokens, dim=3
+    )
+    cross_attention_mask = cross_attention_mask.view(batch_size, text_total_length, -1)
+    cross_attention_mask = cross_attention_mask.unsqueeze(1)
+
+    # invert the mask
+    inverted_cross_attn_mask = (1.0 - cross_attention_mask).to(dtype)
+    cross_attention_mask = inverted_cross_attn_mask.masked_fill(
+        inverted_cross_attn_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+    # apply full-row bias, which return 4D tensor of shape [B, H, S1, 1] where value is 0 if the a full row in cross attn mask's
+    # last dimension contains negative infinity values, otherwise it's 1
+    negative_inf_value = torch.finfo(dtype).min
+    full_text_row_masked_out_mask = (
+        (cross_attention_mask != negative_inf_value)
+        .any(dim=-1)
+        .type_as(cross_attention_mask)[..., None]
+    )
+    cross_attention_mask *= full_text_row_masked_out_mask
+
+    return cross_attention_mask, full_text_row_masked_out_mask
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->MllamaVision
+class MllamaVisionMLP(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = TensorParallelColumnLinear.load(
+            prefix=f"{prefix}.fc1", weights=weights, config=config, bias=True
+        )
+        self.fc2 = TensorParallelRowLinear.load(
+            prefix=f"{prefix}.fc2", weights=weights, config=config, bias=True
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class MllamaVisionSdpaAttention(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+
+        self.embed_dim = config.hidden_size
+        self.head_dim = config.hidden_size // config.attention_heads
+        self.num_heads = config.attention_heads // weights.process_group.size()
+
+        self.qkv_proj = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
+            dim=0,
+            weights=weights,
+            bias=False,
+        )
+        self.o_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.o_proj",
+            weights=weights,
+            bias=False,
+        )
+
+    def forward(
+        self,
+        hidden_state: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        qkv = self.qkv_proj(hidden_state)
+        query, key, value = qkv.split(
+            [
+                self.head_dim * self.num_heads,
+                self.head_dim * self.num_heads,
+                self.head_dim * self.num_heads,
+            ],
+            dim=2,
+        )
+
+        batch_size, q_seq_len, _ = query.shape
+        _, kv_seq_len, _ = key.shape
+
+        query = query.view(batch_size, q_seq_len, self.num_heads, self.head_dim)
+        key = key.view(batch_size, kv_seq_len, self.num_heads, self.head_dim)
+        value = value.view(batch_size, kv_seq_len, self.num_heads, self.head_dim)
+
+        query = query.transpose(1, 2)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+
+        attn_output = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(batch_size, q_seq_len, -1)
+
+        output = self.o_proj(attn_output)
+        return output
+
+
+class MllamaVisionEncoderLayer(nn.Module):
+    def __init__(self, *, prefix, config, weights, is_gated: bool):
+        super().__init__()
+
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.attention_heads
+        self.is_gated = is_gated
+        self.intermediate_size = config.intermediate_size
+
+        self.self_attn = MllamaVisionSdpaAttention(
+            prefix=f"{prefix}.self_attn", config=config, weights=weights
+        )
+        self.mlp = MllamaVisionMLP(
+            prefix=f"{prefix}.mlp", config=config, weights=weights
+        )
+
+        self.input_layernorm = nn.LayerNorm.load(
+            prefix=f"{prefix}.input_layernorm", weights=weights, eps=1e-05
+        )
+        self.post_attention_layernorm = nn.LayerNorm.load(
+            prefix=f"{prefix}.post_attention_layernorm", weights=weights, eps=1e-05
+        )
+
+        # there used to be an if else here, no code path
+        if is_gated:
+            self.gate_attn = nn.Parameter(
+                weights.get_tensor(f"{prefix}.gate_attn"), requires_grad=False
+            )
+            self.gate_ffn = nn.Parameter(
+                weights.get_tensor(f"{prefix}.gate_ffn"), requires_grad=False
+            )
+
+    def forward(
+        self,
+        hidden_state: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        # Self Attention
+        residual = hidden_state
+        hidden_state = self.input_layernorm(hidden_state)
+        hidden_state = self.self_attn(hidden_state, attention_mask=attention_mask)
+        gate_attn = 1 if not self.is_gated else self.gate_attn.tanh()
+        hidden_state = residual + gate_attn * hidden_state
+
+        # Feed forward
+        residual = hidden_state
+        hidden_state = self.post_attention_layernorm(hidden_state)
+        hidden_state = self.mlp(hidden_state)
+        gate_ffn = 1 if not self.is_gated else self.gate_ffn.tanh()
+        hidden_state = residual + gate_ffn * hidden_state
+        return hidden_state
+
+
+class MllamaVisionEncoder(nn.Module):
+    def __init__(self, *, prefix, config, weights, is_gated: bool, num_layers: int):
+        super().__init__()
+        self.config = config
+        self.layers = [
+            MllamaVisionEncoderLayer(
+                prefix=f"{prefix}.layers.{i}",
+                config=config,
+                weights=weights,
+                is_gated=is_gated,
+            )
+            for i in range(num_layers)
+        ]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        encoder_states = [hidden_states]
+        for encoder_layer in self.layers:
+            layer_outputs = encoder_layer(
+                hidden_states,
+                attention_mask,
+            )
+
+            hidden_states = layer_outputs
+            encoder_states.append(hidden_states)
+
+        return hidden_states, encoder_states
+
+
+class MllamaPrecomputedAspectRatioEmbedding(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.max_num_tiles = config.max_num_tiles
+        self.hidden_size = config.hidden_size
+        self.max_aspect_ratio_id = config.max_aspect_ratio_id
+
+        self.embedding = TensorParallelEmbedding(
+            prefix=f"{prefix}.embedding", weights=weights
+        )
+        self.gate = nn.Parameter(
+            weights.get_tensor(f"{prefix}.gate"), requires_grad=False
+        )
+
+    def forward(
+        self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor
+    ) -> torch.Tensor:
+        embeddings = self.embedding(aspect_ratio_ids)
+        embeddings = embeddings.reshape(-1, self.max_num_tiles, 1, self.hidden_size)
+
+        # Always gated.
+        embeddings = embeddings * self.gate.tanh()
+
+        hidden_state = hidden_state + embeddings
+        return hidden_state
+
+
+class MllamaPrecomputedPositionEmbedding(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.max_num_tiles = config.max_num_tiles
+        self.max_aspect_ratio_id = config.max_aspect_ratio_id
+        self.num_patches = (config.image_size // config.patch_size) ** 2 + 1
+        self.hidden_size = config.hidden_size
+        self.scale = config.hidden_size**-0.5
+
+        self.gate = nn.Parameter(
+            weights.get_tensor(f"{prefix}.gate"), requires_grad=False
+        )
+
+        # position embedding
+        embedding = nn.Parameter(
+            weights.get_tensor(f"{prefix}.embedding"), requires_grad=False
+        )
+        self.gated_position_embedding = (1 - self.gate.tanh()) * embedding
+        self.tile_embedding = TensorParallelEmbedding(
+            prefix=f"{prefix}.tile_embedding", weights=weights
+        )
+
+    def forward(
+        self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor
+    ) -> torch.Tensor:
+        # position embeddings
+        hidden_state = hidden_state + self.gated_position_embedding.view(
+            1, 1, self.num_patches, self.hidden_size
+        )
+
+        # precomputed tile position embeddings
+        tile_position_embedding = self.tile_embedding(aspect_ratio_ids)
+        batch_size = hidden_state.shape[0]
+        tile_position_embedding = tile_position_embedding.reshape(
+            batch_size, self.max_num_tiles, self.num_patches, self.hidden_size
+        )
+        gated_tile_position_embedding = self.gate.tanh() * tile_position_embedding
+        hidden_state = hidden_state + gated_tile_position_embedding
+
+        return hidden_state
+
+
+class MllamaVisionModel(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+        self.max_num_tiles = config.max_num_tiles
+        self.hidden_size = config.hidden_size
+        self.num_channels = config.num_channels
+        self.intermediate_layers_indices = config.intermediate_layers_indices
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2 + 1
+        self.scale = config.hidden_size**-0.5
+        self.dtype = weights.dtype
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.hidden_size,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+            bias=False,
+        )
+        self.patch_embedding.weight = nn.Parameter(
+            weights.get_tensor(f"{prefix}.patch_embedding.weight"), requires_grad=False
+        )
+
+        self.class_embedding = nn.Parameter(
+            weights.get_tensor(f"{prefix}.class_embedding"), requires_grad=False
+        )
+
+        self.gated_positional_embedding = MllamaPrecomputedPositionEmbedding(
+            prefix=f"{prefix}.gated_positional_embedding",
+            config=config,
+            weights=weights,
+        )
+
+        self.pre_tile_positional_embedding = MllamaPrecomputedAspectRatioEmbedding(
+            prefix=f"{prefix}.pre_tile_positional_embedding",
+            config=config,
+            weights=weights,
+        )
+        self.post_tile_positional_embedding = MllamaPrecomputedAspectRatioEmbedding(
+            prefix=f"{prefix}.post_tile_positional_embedding",
+            config=config,
+            weights=weights,
+        )
+
+        ## layer norms
+        self.layernorm_pre = nn.LayerNorm.load(
+            prefix=f"{prefix}.layernorm_pre",
+            weights=weights,
+            # torch default
+            eps=1e-05,
+        )
+        self.layernorm_post = nn.LayerNorm.load(
+            prefix=f"{prefix}.layernorm_post",
+            weights=weights,
+            # torch default
+            eps=1e-05,
+        )
+
+        ## encoders
+        self.transformer = MllamaVisionEncoder(
+            prefix=f"{prefix}.transformer",
+            config=config,
+            weights=weights,
+            is_gated=False,
+            num_layers=config.num_hidden_layers,
+        )
+        self.global_transformer = MllamaVisionEncoder(
+            prefix=f"{prefix}.global_transformer",
+            config=config,
+            weights=weights,
+            is_gated=True,
+            num_layers=config.num_global_layers,
+        )
+
+    def apply_class_embedding(self, hidden_state: torch.Tensor) -> torch.Tensor:
+        batch_size, _, hidden_size = hidden_state.shape
+        class_embedding = self.class_embedding.expand(batch_size, 1, hidden_size)
+        hidden_state = torch.cat([class_embedding, hidden_state], dim=1)
+        return hidden_state
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        aspect_ratio_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        batch_size, num_concurrent_media, num_tiles, num_channels, height, width = (
+            pixel_values.shape
+        )
+
+        pixel_values = pixel_values.reshape(
+            batch_size * num_concurrent_media * num_tiles, num_channels, height, width
+        )
+        aspect_ratio_ids = aspect_ratio_ids.reshape(
+            batch_size * num_concurrent_media, -1
+        )
+
+        # patch embedding
+        patch_embeds = self.patch_embedding(pixel_values)
+        hidden_state = patch_embeds.flatten(2).transpose(1, 2)
+
+        # tile embeddings
+        _, num_patches, dim = hidden_state.shape
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media, num_tiles, -1, dim
+        )
+        hidden_state = self.pre_tile_positional_embedding(
+            hidden_state, aspect_ratio_ids
+        )
+
+        # apply cls token
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media * num_tiles, num_patches, dim
+        )
+        hidden_state = self.apply_class_embedding(hidden_state)
+        num_patches += 1
+
+        # apply position embeddings
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media, num_tiles, num_patches, dim
+        )
+        hidden_state = self.gated_positional_embedding(hidden_state, aspect_ratio_ids)
+
+        # apply encoder
+        hidden_state = self.layernorm_pre(hidden_state)
+
+        # Compute the number of tokens to pad
+        num_padding_patches = (8 - (hidden_state.shape[-2] % 8)) % 8
+        # Compute padding tuple for pad function
+        padding = (
+            0,
+            0,
+            0,
+            num_padding_patches,
+        )  # (pad_left, pad_right, pad_left for dim -2, pad_right for dim -2)
+        # Pad the tensor
+        hidden_state = F.pad(hidden_state, padding, mode="constant", value=0)
+        slice_index = -num_padding_patches if num_padding_patches > 0 else None
+
+        if attention_mask is not None:
+            attention_mask = attention_mask.reshape(
+                batch_size * num_concurrent_media, -1
+            )
+            attention_mask = _prepare_aspect_ratio_attention_mask(
+                aspect_ratio_mask=attention_mask,
+                num_patches=self.num_patches,
+                target_length=hidden_state.shape[2],
+                dtype=self.dtype,
+            )
+
+        hidden_state = hidden_state.view(batch_size * num_concurrent_media, -1, dim)
+        hidden_state, all_intermediate_hidden_states = self.transformer(
+            hidden_state,
+            attention_mask=attention_mask,
+        )
+        intermediate_hidden_states = [
+            hidden_state
+            for idx, hidden_state in enumerate(all_intermediate_hidden_states)
+            if idx in self.intermediate_layers_indices
+        ]
+        intermediate_hidden_states = torch.stack(intermediate_hidden_states, dim=-1)
+
+        # apply global encoder
+        hidden_state = self.layernorm_post(hidden_state)
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media,
+            num_tiles,
+            num_patches + num_padding_patches,
+            dim,
+        )
+        hidden_state = self.post_tile_positional_embedding(
+            hidden_state, aspect_ratio_ids
+        )
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media,
+            num_tiles * (num_patches + num_padding_patches),
+            dim,
+        )
+        hidden_state, _ = self.global_transformer(
+            hidden_state, attention_mask=attention_mask
+        )
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media,
+            num_tiles,
+            num_patches + num_padding_patches,
+            dim,
+        )
+        hidden_state = hidden_state[:, :, :slice_index]
+
+        # adding intermediate layer outputs
+        hidden_state = hidden_state.reshape(
+            batch_size, num_concurrent_media, num_tiles, num_patches, dim
+        )
+        intermediate_hidden_states = intermediate_hidden_states.reshape(
+            batch_size * num_concurrent_media,
+            num_tiles,
+            num_patches + num_padding_patches,
+            -1,
+        )
+        intermediate_hidden_states = intermediate_hidden_states[:, :, :slice_index]
+        intermediate_hidden_states = intermediate_hidden_states.reshape(
+            batch_size, num_concurrent_media, num_tiles, num_patches, -1
+        )
+        hidden_state = torch.cat([hidden_state, intermediate_hidden_states], dim=-1)
+        return hidden_state
+
+
+class MllamaTextCrossAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, *, prefix, config, weights, layer_idx):
+        super().__init__()
+        self.config = config
+        self.num_heads = self.config.num_attention_heads
+        self.num_key_value_heads = self.config.num_key_value_heads
+        self.dropout = config.dropout
+        self.hidden_size = config.hidden_size
+        self.head_size = config.hidden_size // self.num_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.layer_idx = layer_idx
+
+        self.num_heads = self.num_heads // weights.process_group.size()
+        self.num_key_value_heads = (
+            self.num_key_value_heads // weights.process_group.size()
+        )
+
+        self.q_proj = TensorParallelColumnLinear.load(
+            config,
+            prefix=f"{prefix}.q_proj",
+            weights=weights,
+            bias=False,
+        )
+        self.k_proj = TensorParallelColumnLinear.load(
+            config,
+            prefix=f"{prefix}.k_proj",
+            weights=weights,
+            bias=False,
+        )
+        self.v_proj = TensorParallelColumnLinear.load(
+            config,
+            prefix=f"{prefix}.v_proj",
+            weights=weights,
+            bias=False,
+        )
+        self.o_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.o_proj",
+            weights=weights,
+            bias=False,
+        )
+
+        self.q_norm = MllamaTextRMSNorm.load(
+            prefix=f"{prefix}.q_norm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.k_norm = MllamaTextRMSNorm.load(
+            prefix=f"{prefix}.k_norm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.softmax_scale = self.head_size**-0.5
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cross_attention_states: Optional[torch.Tensor] = None,
+        # past_key_value=None,
+        # attention_mask: Optional[torch.Tensor] = None,
+        # cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """Input shape: Batch x Time x Channel"""
+        # hidden_states = hidden_states.unsqueeze(0)
+        # bsz, q_len, _ = hidden_states.size()
+        query_states = self.q_proj(hidden_states)
+        query_states = query_states.view(-1, self.num_heads, self.head_size)
+        query_states = self.q_norm(query_states)
+
+        (
+            cross_attention_states,
+            cu_seqlen_q,
+            cu_seqlen_k,
+            max_q,
+            max_k,
+            indices,
+        ) = cross_attention_states
+
+        key_states = self.k_proj(cross_attention_states)
+        value_states = self.v_proj(cross_attention_states)
+        key_states = key_states.view(-1, self.num_key_value_heads, self.head_size)
+        value_states = value_states.view(-1, self.num_key_value_heads, self.head_size)
+        key_states = self.k_norm(key_states)
+
+        # key_states = key_states.repeat(1, self.num_key_value_groups, 1)
+        # value_states = value_states.repeat(1, self.num_key_value_groups, 1)
+
+        causal = False
+        # logger.info(
+        #     f"Q: {query_states.shape} -K {key_states.shape} - V{value_states.shape}"
+        # )
+        if SYSTEM == "ipex":
+            attn_output = torch.empty_like(query_states)
+            ipex.llm.functional.varlen_attention(
+                (
+                    query_states.contiguous()
+                    if query_states.device.type == "xpu"
+                    else query_states
+                ),
+                (
+                    key_states.contiguous()
+                    if key_states.device.type == "xpu"
+                    else key_states
+                ),
+                (
+                    value_states.contiguous()
+                    if value_states.device.type == "xpu"
+                    else value_states
+                ),
+                attn_output,
+                cu_seqlen_q,
+                cu_seqlen_k,
+                max_q,
+                max_k,
+                0.0,
+                self.softmax_scale,
+                False,
+                causal,
+                False,
+                None,
+            )
+        else:
+            attn_output = flash_attn_2_cuda.varlen_fwd(
+                query_states,
+                key_states,
+                value_states,
+                None,
+                cu_seqlen_q,
+                cu_seqlen_k,
+                None,
+                None,
+                None,  # block_tables
+                None,
+                max_q,
+                max_k,
+                0.0,
+                self.softmax_scale,
+                False,
+                causal,  # Causal
+                -1,  # window_size_left,
+                -1,
+                0.0,  # softcap
+                False,
+                None,
+            )[0]
+        attn_output = self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
+
+        return attn_output
+
+
+# Copied from transformers.models.gemma2.modeling_gemma2.Gemma2MLP with Gemma2->MllamaText
+class MllamaTextMLP(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = (
+            config.intermediate_size // weights.process_group.size()
+        )
+        self.gate_up_proj = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
+            weights=weights,
+            dim=0,
+            bias=False,
+        )
+        self.down_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.down_proj",
+            weights=weights,
+            bias=False,
+        )
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        shape = x.shape
+        gate_up_states = self.gate_up_proj(x)
+        gate_up_states = gate_up_states.view(*shape[:-1], 2, self.intermediate_size)
+        result = self.down_proj(
+            self.act_fn(gate_up_states[:, 0]) * gate_up_states[:, 1]
+        )
+        return result
+
+
+class FlashLlamaCrossLayer(torch.nn.Module):
+    """Cross-attention transformer block with tanh-gated attention and feedforward."""
+
+    def __init__(self, *, prefix, config, weights, index) -> None:
+        layer_idx = index
+        super().__init__()
+        self.cross_attn = MllamaTextCrossAttention(
+            prefix=f"{prefix}.cross_attn",
+            config=config,
+            weights=weights,
+            layer_idx=layer_idx,
+        )
+
+        self.input_layernorm = MllamaTextRMSNorm.load(
+            prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.cross_attn_attn_gate = torch.nn.Parameter(
+            weights.get_tensor(f"{prefix}.cross_attn_attn_gate"), requires_grad=False
+        )
+
+        self.mlp = MllamaTextMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
+        self.post_attention_layernorm = MllamaTextRMSNorm.load(
+            prefix=f"{prefix}.post_attention_layernorm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
+        self.cross_attn_mlp_gate = torch.nn.Parameter(
+            weights.get_tensor(f"{prefix}.cross_attn_mlp_gate"), requires_grad=False
+        )
+        self.layer_idx = layer_idx
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+        cos,
+        sin,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        seqlen,
+        max_s,
+        adapter_data,
+        cross_attention_states,  # [ IB, ...]
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if cross_attention_states is None:
+            return hidden_states, residual
+        if residual is not None:
+            hidden_states += residual
+
+        indices = cross_attention_states[-1]
+        out_hidden_states = hidden_states[:]
+        if len(indices) > 0:
+            assert max(indices) < hidden_states.shape[0]
+        hidden_states = hidden_states[indices]
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states = self.cross_attn(
+            hidden_states=hidden_states,
+            # attention_mask=cross_attention_mask,
+            cross_attention_states=cross_attention_states,
+        )
+        hidden_states = residual + self.cross_attn_attn_gate.tanh() * hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + self.cross_attn_mlp_gate.tanh() * hidden_states
+
+        out_hidden_states[indices] = hidden_states
+        hidden_states = out_hidden_states
+
+        return hidden_states, None
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->MllamaText
+class MllamaTextRMSNorm(nn.Module):
+    def __init__(self, weight, eps):
+        super().__init__()
+        self.weight = weight
+        self.variance_epsilon = eps
+
+    @classmethod
+    def load(cls, *, prefix, weights, eps):
+        weight = nn.Parameter(
+            weights.get_tensor(f"{prefix}.weight"), requires_grad=False
+        )
+        return cls(weight=weight, eps=eps)
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class MllamaForConditionalGeneration(nn.Module):
+    def __init__(self, prefix, config, weights):
+        super().__init__()
+        config.vision_config.quantize = None
+        config.vision_config.speculator = config.speculator
+        config.text_config.quantize = config.quantize
+        config.text_config.speculator = config.speculator
+        config.text_config._attn_implementation = "sdpa"
+        self.hidden_size = config.text_config.hidden_size
+        self.vision_model = MllamaVisionModel(
+            prefix="vision_model", config=config.vision_config, weights=weights
+        )
+        self.multi_modal_projector = FastLinear.load(
+            prefix="multi_modal_projector", config=config, weights=weights, bias=True
+        )
+        self.text_model = FlashLlamaForCausalLM(
+            prefix="language_model", config=config.text_config, weights=weights
+        )
+        self.config = config
+        self.dtype = weights.dtype
+        self.device = weights.device
+
+    def vision_forward(self, pixel_values, aspect_ratio_ids, aspect_ratio_mask):
+        if aspect_ratio_ids is None:
+            raise ValueError(
+                "`aspect_ratio_ids` must be provided if `pixel_values` is provided"
+            )
+        # logger.info(f"PIxel values {pixel_values.shape}")
+        batch_size = pixel_values.shape[0]
+        vision_states = self.vision_model(
+            pixel_values, aspect_ratio_ids, aspect_ratio_mask
+        )
+        cross_attention_states = self.multi_modal_projector(vision_states).reshape(
+            -1, vision_states.shape[-2], self.hidden_size
+        )
+        _, _, h = cross_attention_states.shape
+        cross_attention_states = cross_attention_states.view(batch_size, -1, h)
+        # logger.info(f"cross {cross_attention_states.shape}")
+        return cross_attention_states
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        seqlen: Seqlen,
+        max_s: int,
+        prefill_cache_indices: Optional[torch.Tensor],
+        lm_head_indices: Optional[torch.Tensor],
+        adapter_data: Optional[torch.Tensor] = None,
+        # XXX: Putting these as optional so that the cuda warmup calls can go through.
+        cross_attention_states: Optional[torch.Tensor] = None,
+        image_indices=None,
+    ):
+        if cross_attention_states is not None:
+            seqlen_q = len(image_indices)
+            n_images = cross_attention_states.shape[0]
+            seqlen_k = cross_attention_states.shape[1]
+            device = cross_attention_states.device
+            if cu_seqlen_prefill is not None:
+                offset = 0
+                cu_q = []
+                indices = []
+                for index in image_indices:
+                    cu_q.append(offset)
+                    length = seqlen.input_lengths[index].item()
+                    assert index < seqlen.cu_seqlen_q.shape[0]
+                    input_ids_offset = seqlen.cu_seqlen_q[index]
+                    indices.extend(range(input_ids_offset, input_ids_offset + length))
+                    offset += length
+                cu_q.append(offset)
+                cu_seqlen_q = torch.Tensor(cu_q).to(device=device, dtype=torch.int32)
+
+                assert max(indices) < input_ids.shape[0]
+
+                cu_seqlen_k = (
+                    torch.arange(
+                        n_images + 1,
+                        device=device,
+                        dtype=torch.int32,
+                    )
+                    * seqlen_k
+                )
+                max_q = cu_seqlen_q[-1].item()
+                max_k = seqlen_k
+            else:
+                cu_seqlen_q = torch.arange(
+                    seqlen_q + 1, device=device, dtype=torch.int32
+                )
+                seqlen_k = cross_attention_states.shape[1]
+                n_images = cross_attention_states.shape[0]
+                cu_seqlen_k = (
+                    torch.arange(
+                        n_images + 1,
+                        device=device,
+                        dtype=torch.int32,
+                    )
+                    * seqlen_k
+                )
+                max_q = seqlen_q
+                max_k = seqlen_k
+                indices = image_indices[:]
+
+            cross_attention_states = (
+                cross_attention_states,
+                cu_seqlen_q,
+                cu_seqlen_k,
+                max_q,
+                max_k,
+                indices,
+            )
+
+        outputs = self.text_model(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlen_prefill=cu_seqlen_prefill,
+            kv_cache=kv_cache,
+            block_tables=block_tables,
+            slots=slots,
+            seqlen=seqlen,
+            max_s=max_s,
+            prefill_cache_indices=prefill_cache_indices,
+            lm_head_indices=lm_head_indices,
+            adapter_data=adapter_data,
+            cross_attention_states=cross_attention_states,
+        )
+
+        return outputs
diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py
index a2834962..33fe30a8 100644
--- a/server/text_generation_server/models/flash_causal_lm.py
+++ b/server/text_generation_server/models/flash_causal_lm.py
@@ -46,7 +46,7 @@ from text_generation_server.models.globals import (
     TGI_WIGGLE_ROOM,
     get_adapter_to_index,
 )
-from text_generation_server.layers.attention import Seqlen
+from text_generation_server.layers.attention import KVCache, Seqlen
 from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
 from text_generation_server.utils.dist import MEMORY_FRACTION
 from text_generation_server.utils.quantization import get_loader
@@ -937,6 +937,7 @@ class FlashCausalLM(Model):
         # Deepseek V2 uses different QK and V dims.
         head_size: Optional[int] = None,
         skip_special_tokens: bool = True,
+        kv_cache_dtype: Optional[torch.dtype] = None,
     ):
         self.quantize = quantize
         self.process_group, rank, world_size = initialize_torch_distributed()
@@ -1034,6 +1035,7 @@ class FlashCausalLM(Model):
 
         self.cuda_graphs = {}
         self.kv_cache = []
+        self.kv_cache_dtype = dtype if kv_cache_dtype is None else kv_cache_dtype
 
         if ATTENTION == "flashinfer":
             from text_generation_server.layers.attention.flashinfer import (
@@ -1083,61 +1085,16 @@ class FlashCausalLM(Model):
     ):
         self.kv_cache = []
         empty_cache()
-
-        element_size = torch.tensor([], dtype=dtype).element_size()
-        if SYSTEM == "ipex" and device.type == "xpu":
-            x = 1
-        else:
-            x = BLOCK_SIZE // element_size
-
-        if ATTENTION in {"flashdecoding", "flashinfer"}:
-            self.kv_cache = [
-                (
-                    torch.empty(
-                        (num_blocks, BLOCK_SIZE, num_heads, head_size),
-                        dtype=dtype,
-                        device=device,
-                    ),
-                    torch.empty(
-                        (num_blocks, BLOCK_SIZE, num_heads, head_size),
-                        dtype=dtype,
-                        device=device,
-                    ),
-                )
-                for _ in range(num_layers)
-            ]
-        elif SYSTEM == "ipex" and device == torch.device("cpu"):
-            self.kv_cache = [
-                (
-                    torch.empty(
-                        (num_blocks, num_heads, BLOCK_SIZE, head_size),
-                        dtype=dtype,
-                        device=device,
-                    ),
-                    torch.empty(
-                        (num_blocks, num_heads, BLOCK_SIZE, head_size),
-                        dtype=dtype,
-                        device=device,
-                    ),
-                )
-                for _ in range(num_layers)
-            ]
-        else:
-            self.kv_cache = [
-                (
-                    torch.empty(
-                        (num_blocks, num_heads, head_size // x, BLOCK_SIZE, x),
-                        dtype=dtype,
-                        device=device,
-                    ),
-                    torch.empty(
-                        (num_blocks, num_heads, head_size, BLOCK_SIZE),
-                        dtype=dtype,
-                        device=device,
-                    ),
-                )
-                for _ in range(num_layers)
-            ]
+        self.kv_cache = [
+            KVCache(
+                num_blocks=num_blocks,
+                num_heads=num_heads,
+                head_size=head_size,
+                dtype=dtype,
+                device=device,
+            )
+            for _ in range(num_layers)
+        ]
 
     def cuda_graph_warmup(self, bs: int, max_s: int, max_bt: int):
         input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device)
@@ -1258,7 +1215,7 @@ class FlashCausalLM(Model):
                 self.num_layers,
                 self.num_kv_heads,
                 self.head_size,
-                self.dtype,
+                self.kv_cache_dtype,
                 self.device,
             )
             max_bt = batch.max_blocks
@@ -1277,7 +1234,7 @@ class FlashCausalLM(Model):
 
         # Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm)
         # Calculate the number of blocks that can be allocated with the free memory
-        dtype_size = torch.tensor([], dtype=self.dtype).element_size()
+        dtype_size = torch.tensor([], dtype=self.kv_cache_dtype).element_size()
         cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size
         total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size
 
@@ -1291,6 +1248,8 @@ class FlashCausalLM(Model):
             + batch_num_blocks
         )
 
+        log_master(logger.info, f"KV-cache blocks: {num_blocks}, size: {BLOCK_SIZE}")
+
         del batch
 
         self.init_kv_cache(
@@ -1298,7 +1257,7 @@ class FlashCausalLM(Model):
             self.num_layers,
             self.num_kv_heads,
             self.head_size,
-            self.dtype,
+            self.kv_cache_dtype,
             self.device,
         )
 
@@ -1320,8 +1279,7 @@ class FlashCausalLM(Model):
                 elif CUDA_GRAPHS is not None:
                     tuning_sequences = CUDA_GRAPHS
                 else:
-                    # For seqlen = 1, we dispatch to LLMM1 kernel.
-                    tuning_sequences = [2, 3, 4, 5, 6, 7]
+                    tuning_sequences = [1, 2, 3, 4, 5, 6, 7]
 
                 tunableop_filepath = os.path.join(
                     HUGGINGFACE_HUB_CACHE,
@@ -1330,7 +1288,11 @@ class FlashCausalLM(Model):
 
                 log_master(
                     logger.info,
-                    f"PyTorch TunableOp (https://github.com/fxmarty/pytorch/tree/2.3-patched/aten/src/ATen/cuda/tunable) is enabled. The warmup may take several minutes, picking the ROCm optimal matrix multiplication kernel for the target lengths {', '.join([str(seqlen) for seqlen in tuning_sequences])}, with typical 5-8% latency improvement for small sequence lengths. The picked GEMMs are saved in the file {tunableop_filepath}. To disable TunableOp, please launch TGI with `PYTORCH_TUNABLEOP_ENABLED=0`.",
+                    f"PyTorch TunableOp is enabled. The warmup may take several minutes, picking the ROCm optimal matrix multiplication kernel for the target lengths {', '.join([str(seqlen) for seqlen in tuning_sequences])}, with typical 5-8% latency improvement for small sequence lengths. The picked GEMMs are saved in the file {tunableop_filepath}. To disable TunableOp, please launch TGI with `PYTORCH_TUNABLEOP_ENABLED=0`.",
+                )
+
+                torch.cuda.tunable.set_filename(
+                    tunableop_filepath, insert_device_ordinal=False
                 )
 
                 if os.path.isfile(tunableop_filepath):
@@ -1346,7 +1308,8 @@ class FlashCausalLM(Model):
                     log_master(logger.info, f"Warming up TunableOp for seqlen={seqlen}")
                     self.tunableop_warmup(seqlen)
                     torch.cuda.tunable.write_file(tunableop_filepath)
-                torch.cuda.tunable.tuning_enable(False)
+                if os.environ.get("PYTORCH_TUNABLEOP_TUNING_AFTER_WARMUP") != "1":
+                    torch.cuda.tunable.tuning_enable(False)
             else:
                 log_master(
                     logger.info,
@@ -1382,6 +1345,7 @@ class FlashCausalLM(Model):
         cu_seqlen_prefill = torch.tensor(
             [0, seqlen], device=self.device, dtype=torch.int32
         )
+        max_s = seqlen
         seqlen = Seqlen(
             input_lengths=input_lengths,
             prefix_lengths=prefix_lens_tensor,
@@ -1399,7 +1363,7 @@ class FlashCausalLM(Model):
             block_tables=None,
             seqlen=seqlen,
             slots=slots,
-            max_s=seqlen,
+            max_s=max_s,
             lm_head_indices=None,
             prefill_cache_indices=None,
         )
@@ -1960,6 +1924,8 @@ class FlashCausalLM(Model):
                 num_kv_heads=self.num_kv_heads,
                 head_size=self.head_size,
                 page_size=BLOCK_SIZE,
+                dtype=self.dtype,
+                window_left=self.sliding_window,
             )
         else:
             assert input_lengths_tensor is not None
@@ -1971,6 +1937,8 @@ class FlashCausalLM(Model):
                 num_kv_heads=self.num_kv_heads,
                 head_size=self.head_size,
                 page_size=BLOCK_SIZE,
+                dtype=self.dtype,
+                window_left=self.sliding_window,
             )
 
 
diff --git a/server/text_generation_server/models/idefics.py b/server/text_generation_server/models/idefics.py
deleted file mode 100644
index 9058cb96..00000000
--- a/server/text_generation_server/models/idefics.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import torch
-import torch.distributed
-
-from typing import Optional
-
-
-from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
-from text_generation_server.models.custom_modeling.idefics_processing import (
-    IdeficsProcessor,
-)
-from transformers import LlamaTokenizerFast
-from text_generation_server.models.custom_modeling.idefics_modeling import (
-    IdeficsForVisionText2Text,
-)
-from text_generation_server.models.idefics_causal_lm import IdeficsCausalLM
-from text_generation_server.utils import (
-    initialize_torch_distributed,
-    weight_files,
-    Weights,
-)
-from text_generation_server.utils.quantization import get_loader
-
-from text_generation_server.utils.import_utils import SYSTEM
-
-
-class IDEFICSSharded(IdeficsCausalLM):
-    def __init__(
-        self,
-        model_id: str,
-        revision: Optional[str] = None,
-        quantize: Optional[str] = None,
-        speculator: Optional[str] = None,
-        dtype: Optional[torch.dtype] = None,
-        trust_remote_code: bool = False,
-    ):
-        self.quantize = quantize
-        self.process_group, rank, world_size = initialize_torch_distributed()
-        if torch.cuda.is_available():
-            device = torch.device(f"cuda:{rank}")
-            # 9b seems to work correctly enough in float16, but 80b seems
-            # to be really saturating for f16.
-            dtype = torch.float16 if dtype is None else dtype
-        elif SYSTEM == "ipex":
-            if hasattr(torch, "xpu") and torch.xpu.is_available():
-                device = torch.device(f"xpu:{rank}")
-                dtype = torch.float16 if dtype is None else dtype
-            else:
-                device = torch.device("cpu")
-                # Float16 doesn't exist on target.
-                dtype = torch.bfloat16 if dtype is None else dtype
-        else:
-            device = torch.device("cpu")
-            dtype = torch.float32 if dtype is None else dtype
-        self.device, self.dtype = device, dtype
-
-        config = IdeficsConfig.from_pretrained(
-            model_id,
-            revision=revision,
-            trust_remote_code=trust_remote_code,
-        )
-        config.quantize = quantize
-        config.speculator = speculator
-        config.vision_config.quantize = quantize
-
-        tokenizer = LlamaTokenizerFast.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
-        self.processor = IdeficsProcessor.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
-
-        weights_loader = get_loader(
-            quantize=quantize, model_id=model_id, revision=revision
-        )
-        torch.distributed.barrier(group=self.process_group)
-        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
-        weights = Weights(
-            filenames,
-            device=device,
-            dtype=dtype,
-            process_group=self.process_group,
-            weights_loader=weights_loader,
-        )
-
-        model = IdeficsForVisionText2Text(config, weights)
-
-        torch.distributed.barrier(group=self.process_group)
-        super(IdeficsCausalLM, self).__init__(
-            model_id=model_id,
-            model=model,
-            tokenizer=tokenizer,
-            requires_padding=True,
-            dtype=dtype,
-            device=device,
-            rank=rank,
-            world_size=world_size,
-        )
diff --git a/server/text_generation_server/models/idefics_causal_lm.py b/server/text_generation_server/models/idefics_causal_lm.py
index c5480952..9a7a6fe1 100644
--- a/server/text_generation_server/models/idefics_causal_lm.py
+++ b/server/text_generation_server/models/idefics_causal_lm.py
@@ -6,6 +6,7 @@ import time
 from dataclasses import dataclass
 from opentelemetry import trace
 from transformers import (
+    AutoConfig,
     AutoProcessor,
     AutoTokenizer,
     PreTrainedTokenizerBase,
@@ -22,6 +23,18 @@ from text_generation_server.models.types import (
 )
 from text_generation_server.pb import generate_pb2
 from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
+import torch.distributed
+from text_generation_server.models.custom_modeling.idefics_modeling import (
+    IdeficsForVisionText2Text,
+)
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+    Weights,
+)
+from text_generation_server.utils.quantization import get_loader
+
+from text_generation_server.utils.import_utils import SYSTEM
 
 
 tracer = trace.get_tracer(__name__)
@@ -577,23 +590,38 @@ class IdeficsCausalLM(Model):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        speculator: Optional[str] = None,
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         self.quantize = quantize
-        from text_generation_server.models.custom_modeling.idefics_modeling import (
-            IdeficsForVisionText2Text,
-        )
-
+        self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
-            device = torch.device("cuda")
-            dtype = torch.bfloat16 if dtype is None else dtype
+            device = torch.device(f"cuda:{rank}")
+            # 9b seems to work correctly enough in float16, but 80b seems
+            # to be really saturating for f16.
+            dtype = torch.float16 if dtype is None else dtype
+        elif SYSTEM == "ipex":
+            if hasattr(torch, "xpu") and torch.xpu.is_available():
+                device = torch.device(f"xpu:{rank}")
+                dtype = torch.float16 if dtype is None else dtype
+            else:
+                device = torch.device("cpu")
+                # Float16 doesn't exist on target.
+                dtype = torch.bfloat16 if dtype is None else dtype
         else:
-            if quantize:
-                raise ValueError("quantization is not available on CPU")
-
             device = torch.device("cpu")
             dtype = torch.float32 if dtype is None else dtype
+        self.device, self.dtype = device, dtype
+
+        config = AutoConfig.from_pretrained(
+            model_id,
+            revision=revision,
+            trust_remote_code=trust_remote_code,
+        )
+        config.quantize = quantize
+        config.speculator = speculator
+        config.vision_config.quantize = quantize
 
         tokenizer = AutoTokenizer.from_pretrained(
             model_id,
@@ -609,38 +637,34 @@ class IdeficsCausalLM(Model):
             truncation_side="left",
             trust_remote_code=trust_remote_code,
         )
-        model = IdeficsForVisionText2Text.from_pretrained(
-            model_id,
-            revision=revision,
-            torch_dtype=dtype,
-            device_map=(
-                "auto"
-                if torch.cuda.is_available() and torch.cuda.device_count() > 1
-                else None
-            ),
-            load_in_8bit=quantize == "bitsandbytes",
-            trust_remote_code=trust_remote_code,
+
+        weights_loader = get_loader(
+            quantize=quantize, model_id=model_id, revision=revision
+        )
+        torch.distributed.barrier(group=self.process_group)
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+        weights = Weights(
+            filenames,
+            device=device,
+            dtype=dtype,
+            process_group=self.process_group,
+            weights_loader=weights_loader,
         )
-        if torch.cuda.is_available() and torch.cuda.device_count() == 1:
-            model = model.cuda()
 
-        if tokenizer.pad_token_id is None:
-            if model.config.pad_token_id is not None:
-                tokenizer.pad_token_id = model.config.pad_token_id
-            elif model.config.eos_token_id is not None:
-                tokenizer.pad_token_id = model.config.eos_token_id
-            elif tokenizer.eos_token_id is not None:
-                tokenizer.pad_token_id = tokenizer.eos_token_id
-            else:
-                tokenizer.add_special_tokens({"pad_token": "<unk>"})
+        model = IdeficsForVisionText2Text(config, weights)
 
-        super(IdeficsCausalLM, self).__init__(
+        self.config = config
+
+        torch.distributed.barrier(group=self.process_group)
+        super().__init__(
             model_id=model_id,
             model=model,
             tokenizer=tokenizer,
             requires_padding=True,
             dtype=dtype,
             device=device,
+            rank=rank,
+            world_size=world_size,
         )
 
     @property
diff --git a/server/text_generation_server/models/mllama_causal_lm.py b/server/text_generation_server/models/mllama_causal_lm.py
new file mode 100644
index 00000000..9e19e171
--- /dev/null
+++ b/server/text_generation_server/models/mllama_causal_lm.py
@@ -0,0 +1,357 @@
+from io import BytesIO
+from PIL import Image
+import torch
+from typing import Iterable, Optional, Tuple, List, Dict
+from text_generation_server.pb.generate_pb2 import Request
+
+from dataclasses import dataclass
+from opentelemetry import trace
+from transformers import (
+    PreTrainedTokenizerBase,
+)
+from text_generation_server.models.vlm_causal_lm import VlmCausalLMBatch, VlmCausalLM
+from text_generation_server.pb import generate_pb2
+from text_generation_server.models.flash_causal_lm import (
+    block_tables_to_ragged,
+)
+from text_generation_server.models.globals import PREFIX_CACHING, ATTENTION
+from text_generation_server.layers.attention import Seqlen
+
+
+tracer = trace.get_tracer(__name__)
+
+
+@dataclass
+class MllamaCausalLMBatch(VlmCausalLMBatch):
+    image_indices: List[int] = 42
+    aspect_ratio_ids: Optional[torch.Tensor] = None
+    aspect_ratio_mask: Optional[torch.Tensor] = None
+    cross_attention_states: Optional[torch.Tensor] = None
+
+    @classmethod
+    @tracer.start_as_current_span("concatenate")
+    def concatenate(cls, batches):
+        batch = super().concatenate(batches)
+        batch.pixel_values = None
+        batch.pixel_attention_mask = None
+
+        offset = 0
+        image_indices = []
+        attention_states = []
+        for b in batches:
+            if b.cross_attention_states is not None:
+                attention_states.append(b.cross_attention_states)
+            image_indices.extend([i + offset for i in b.image_indices])
+            offset += len(b.image_indices)
+        if len(attention_states) > 0:
+            assert len(image_indices) > 0
+            batch.cross_attention_states = torch.cat(attention_states, dim=0)
+            batch.image_indices = image_indices
+        else:
+            batch.cross_attention_states = None
+            batch.image_indices = []
+        return batch
+
+    @tracer.start_as_current_span("filter")
+    def filter(self, request_ids: List[int]):
+        assert self.image_indices is not None
+        batch = super().filter(request_ids)
+        assert self.image_indices is not None
+        indices = []
+        for i, request_id in enumerate(request_ids):
+            idx = self.requests_idx_mapping[request_id]
+            indices.append(idx)
+
+        offset = 0
+        new_image_indices = []
+        prev_i = None
+        for i in self.image_indices:
+            if i in indices:
+                new_image_indices.append(offset)
+                if i != prev_i:
+                    offset += 1
+                prev_i = i
+
+        batch.image_indices = new_image_indices
+        if len(new_image_indices) > 0:
+            assert max(new_image_indices) < self.cross_attention_states.shape[0]
+            assert offset <= self.cross_attention_states.shape[0]
+            batch.cross_attention_states = self.cross_attention_states[
+                new_image_indices
+            ]
+        else:
+            batch.cross_attention_states = None
+        return batch
+
+    @classmethod
+    def batch_tokenized_inputs(
+        cls, requests: Iterable[Request], tokenizer, processor, config
+    ):
+        image_inputs = []
+        texts = []
+        image_indices = []
+        batch_tokenized_inputs = []
+
+        for i, r in enumerate(requests):
+            # Each input is encoded into a list, where each element of this input list is either a string or a URL
+            curr_text = ""
+            curr_image = None
+            curr_i = None
+            for chunk in r.input_chunks.chunks:
+                chunk_type = chunk.WhichOneof("chunk")
+                if chunk_type == "text":
+                    curr_text += chunk.text
+                elif chunk_type == "image":
+                    image = Image.open(BytesIO(chunk.image.data))
+                    # TODO unsure about BOS
+                    curr_text += "<|image|>"
+                    image_input = processor.image_processor(image, return_tensors="pt")
+                    curr_image = image_input
+                    curr_i = i
+                    # image_inputs.append(image_input)
+                    # image_indices.append(i)
+                else:
+                    raise RuntimeError(f"Invalid chunk type {chunk_type}")
+            texts.append(curr_text)
+            if curr_image is not None:
+                image_inputs.append(curr_image)
+                image_indices.append(curr_i)
+
+            input_ids = tokenizer(
+                curr_text,
+                truncation=True,
+                max_length=r.truncate,
+                add_special_tokens=r.add_special_tokens,
+            )["input_ids"]
+            batch_tokenized_inputs.append(input_ids)
+        if image_inputs:
+            image_input = image_inputs[0]
+            new_image_inputs = {
+                "pixel_values": torch.cat(
+                    [img["pixel_values"] for img in image_inputs], dim=0
+                ),
+            }
+            if "aspect_ratio_ids" in image_input:
+                new_image_inputs["aspect_ratio_ids"] = torch.cat(
+                    [img["aspect_ratio_ids"] for img in image_inputs], dim=0
+                )
+            if "aspect_ratio_mask" in image_input:
+                new_image_inputs["aspect_ratio_mask"] = torch.cat(
+                    [img["aspect_ratio_mask"] for img in image_inputs], dim=0
+                )
+            image_inputs = new_image_inputs
+            image_inputs["image_indices"] = image_indices
+        else:
+            image_inputs = None
+
+        if image_inputs is not None:
+            assert len(image_indices) == image_inputs["pixel_values"].shape[0]
+
+        return batch_tokenized_inputs, image_inputs
+
+    @classmethod
+    def from_pb_processor(
+        cls,
+        pb: generate_pb2.Batch,
+        tokenizer: PreTrainedTokenizerBase,
+        processor,
+        config,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> "VlmCausalLMBatch":
+        batch_tokenized_inputs, image_inputs = cls.batch_tokenized_inputs(
+            pb.requests, tokenizer, processor, config
+        )
+        batch = cls.from_tokenized(pb, tokenizer, batch_tokenized_inputs, dtype, device)
+        # XXX: <|image|> token is actually out of bounds and bugs out the logit processors.
+        batch.all_input_ids_tensor = batch.all_input_ids_tensor.clamp(
+            max=config.text_config.vocab_size - 1
+        )
+        batch.input_ids = batch.input_ids.clamp(max=config.text_config.vocab_size - 1)
+
+        if image_inputs is not None:
+            batch.pixel_values = image_inputs["pixel_values"].to(
+                device=device, dtype=dtype
+            )
+            batch.aspect_ratio_ids = image_inputs["aspect_ratio_ids"].to(device=device)
+            batch.aspect_ratio_mask = image_inputs["aspect_ratio_mask"].to(
+                device=device
+            )
+            batch.image_indices = image_inputs["image_indices"]
+        else:
+            batch.pixel_values = None
+            batch.aspect_ratio_ids = None
+            batch.aspect_ratio_mask = None
+            batch.image_indices = []
+        assert batch.image_indices is not None
+        return batch
+
+
+class MllamaCausalLM(VlmCausalLM):
+    def forward(
+        self,
+        batch: VlmCausalLMBatch,
+        adapter_data: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        # Model Forward
+        if batch.speculative_ids is not None:
+            input_ids = batch.input_ids
+            position_ids = batch.position_ids
+            cu_seqlen_prefill = batch.cu_seqlen_prefill
+            kv_cache = self.kv_cache
+            block_tables = batch.block_tables_tensor
+            slots = batch.slots[batch.slot_indices]
+            input_lengths = batch.input_lengths_tensor
+            max_s = batch.max_seqlen
+            lm_head_indices = batch.prefill_head_indices
+
+            speculative_ids = batch.speculative_ids
+
+            B, speculative_length = speculative_ids.shape
+            new_length = speculative_length + 1
+            new_input_ids = torch.cat(
+                [input_ids.unsqueeze(-1), speculative_ids], dim=1
+            ).reshape(-1)
+            arange = torch.arange(new_length, device=position_ids.device).unsqueeze(0)
+            arange_int = arange.to(dtype=torch.int32)
+            new_position_ids = (
+                position_ids.unsqueeze(-1).expand(B, new_length) + arange
+            ).view(-1)
+            slots = (slots.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1)
+            input_lengths = (
+                input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int
+            ).view(-1)
+            prefix_lens_tensor = (
+                batch.prefix_lens_tensor.unsqueeze(-1).expand(B, new_length)
+            ).reshape(-1)
+
+            # Add Copy the block tables for all members
+            block_tables = (
+                block_tables.unsqueeze(1)
+                .expand(B, new_length, -1)
+                .reshape(B * new_length, -1)
+                .contiguous()
+            )
+            max_s = max_s + speculative_length
+
+            input_ids = new_input_ids
+            position_ids = new_position_ids
+        else:
+            input_ids = batch.input_ids
+            position_ids = batch.position_ids
+            cu_seqlen_prefill = batch.cu_seqlen_prefill
+            kv_cache = self.kv_cache
+            block_tables = batch.block_tables_tensor
+            slots = batch.slots[batch.slot_indices]
+            input_lengths = batch.input_lengths_tensor
+            prefix_lens_tensor = batch.prefix_lens_tensor
+            max_s = batch.max_seqlen
+            lm_head_indices = batch.prefill_head_indices
+
+        if cu_seqlen_prefill is None and self.max_past() is not None:
+            # In decode, not prefill, we're actually overwriting the KV-cache
+            # in a circular buffer mode.
+            # This makes sure the max_s for the decode pass is correct.
+            max_s = min(self.max_past(), max_s)
+
+        bs = input_ids.shape[0]
+        # Try to find an associated cuda graph
+        bs = input_ids.shape[0]
+        sorted_padded_bs = sorted([k for k in self.cuda_graphs.keys() if k >= bs])
+        if sorted_padded_bs:
+            # Get associated cuda graph
+            cuda_graph = self.cuda_graphs[sorted_padded_bs[0]]
+        else:
+            cuda_graph = None
+        if (
+            cu_seqlen_prefill is not None
+            or cuda_graph is None
+            # Only run cuda graphs when there's no images.
+            or batch.cross_attention_states is not None
+        ):
+            input_lengths = input_lengths + prefix_lens_tensor
+            if PREFIX_CACHING:
+                block_tables = block_tables_to_ragged(
+                    block_tables=block_tables,
+                    input_lengths=batch.input_lengths,
+                    prefix_lens=batch.prefix_lens,
+                )
+            with self._forward_context(
+                block_tables=block_tables,
+                cu_seqlen_prefill=cu_seqlen_prefill,
+                input_lengths_tensor=input_lengths,
+                prefix_lens_tensor=prefix_lens_tensor,
+            ):
+                max_k = (input_lengths + prefix_lens_tensor).max().item()
+                seqlen = Seqlen(
+                    input_lengths=input_lengths,
+                    prefix_lengths=prefix_lens_tensor,
+                    cu_seqlen_q=cu_seqlen_prefill,
+                    max_q=max_s,
+                    max_k=max_k,
+                )
+
+                if batch.pixel_values is not None:
+                    cross_attention_states = self.model.vision_forward(
+                        pixel_values=batch.pixel_values,
+                        aspect_ratio_ids=batch.aspect_ratio_ids,
+                        aspect_ratio_mask=batch.aspect_ratio_mask,
+                    )
+                    batch.cross_attention_states = cross_attention_states
+
+                cross_attention_states = batch.cross_attention_states
+
+                logits, speculative_logits = self.model.forward(
+                    input_ids=input_ids,
+                    position_ids=position_ids,
+                    cu_seqlen_prefill=cu_seqlen_prefill,
+                    kv_cache=kv_cache,
+                    block_tables=block_tables,
+                    slots=slots,
+                    seqlen=seqlen,
+                    max_s=max_s,
+                    prefill_cache_indices=batch.prefill_cache_indices,
+                    lm_head_indices=lm_head_indices,
+                    cross_attention_states=cross_attention_states,
+                    adapter_data=adapter_data,
+                    image_indices=batch.image_indices[:],
+                )
+                if batch.prefill_cache_indices is not None:
+                    batch.prefill_cache_indices = None
+                if batch.pixel_values is not None:
+                    batch.pixel_values = None
+                return logits, speculative_logits
+
+        # Copy inputs to the static inputs of the cuda graph
+        # Static inputs are potentially padded
+        cuda_graph["input_ids"][: input_ids.shape[0]] = input_ids
+        cuda_graph["position_ids"][: position_ids.shape[0]] = position_ids
+        if ATTENTION == "flashinfer":
+            block_tables = block_tables_to_ragged(
+                block_tables=block_tables,
+                input_lengths=batch.input_lengths,
+                prefix_lens=batch.prefix_lens,
+            )
+            cuda_graph["block_tables"][: block_tables.shape[0]] = block_tables
+        else:
+            cuda_graph["block_tables"][
+                : block_tables.shape[0], : block_tables.shape[1]
+            ] = block_tables
+        cuda_graph["slots"].fill_(0)
+        cuda_graph["slots"][: slots.shape[0]] = slots
+        cuda_graph["input_lengths"].zero_()
+        cuda_graph["input_lengths"][: input_lengths.shape[0]] = (
+            input_lengths + prefix_lens_tensor
+        )
+
+        # Replay the graph
+        cuda_graph["graph"].replay()
+
+        # Slice output to the correct shape
+        speculative_logits = (
+            cuda_graph["speculative_logits"][:bs]
+            if cuda_graph["speculative_logits"] is not None
+            else None
+        )
+        logits = cuda_graph["logits"][:bs]
+        return logits, speculative_logits
diff --git a/server/text_generation_server/models/seq2seq_lm.py b/server/text_generation_server/models/seq2seq_lm.py
index 04d4c28b..91c99c50 100644
--- a/server/text_generation_server/models/seq2seq_lm.py
+++ b/server/text_generation_server/models/seq2seq_lm.py
@@ -558,14 +558,13 @@ class Seq2SeqLM(Model):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = default_dtype if dtype is None else dtype
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            device = torch.device(f"xpu:{rank}")
+            dtype = default_dtype if dtype is None else dtype
         elif SYSTEM == "ipex":
-            if hasattr(torch, "xpu") and torch.xpu.is_available():
-                device = torch.device(f"xpu:{rank}")
-                dtype = default_dtype if dtype is None else dtype
-            else:
-                device = torch.device("cpu")
-                # Float16 doesn't exist on target.
-                dtype = torch.bfloat16 if dtype is None else dtype
+            device = torch.device("cpu")
+            # Float16 doesn't exist on target.
+            dtype = torch.bfloat16 if dtype is None else dtype
         else:
             device = torch.device("cpu")
             dtype = torch.float32 if dtype is None else dtype
@@ -630,8 +629,14 @@ class Seq2SeqLM(Model):
         if speculator:
             raise RuntimeError("Speculator decoding is not enabled for AutoModel")
 
+        device_count = 0
         if torch.cuda.is_available():
             device = torch.device("cuda")
+            device_count = torch.cuda.device_count()
+            dtype = torch.float16 if dtype is None else dtype
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            device = torch.device("xpu")
+            device_count = torch.xpu.device_count()
             dtype = torch.float16 if dtype is None else dtype
         else:
             if quantize:
@@ -644,16 +649,12 @@ class Seq2SeqLM(Model):
             model_id,
             revision=revision,
             torch_dtype=dtype,
-            device_map=(
-                "auto"
-                if torch.cuda.is_available() and torch.cuda.device_count() > 1
-                else None
-            ),
+            device_map=("auto" if device_count > 1 else None),
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )
-        if torch.cuda.is_available() and torch.cuda.device_count() == 1:
-            model = model.cuda()
+        if device_count == 1:
+            model = model.to(device)
 
         tokenizer = AutoTokenizer.from_pretrained(
             model_id,
diff --git a/server/text_generation_server/server.py b/server/text_generation_server/server.py
index 22871ec5..46e342a4 100644
--- a/server/text_generation_server/server.py
+++ b/server/text_generation_server/server.py
@@ -22,8 +22,14 @@ try:
         VlmCausalLMBatch,
     )
     from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
+    from text_generation_server.models.mllama_causal_lm import MllamaCausalLMBatch
 
-    VLM_BATCH_TYPES = {PaliGemmaBatch, VlmCausalLMBatch, IdeficsCausalLMBatch}
+    VLM_BATCH_TYPES = {
+        PaliGemmaBatch,
+        VlmCausalLMBatch,
+        IdeficsCausalLMBatch,
+        MllamaCausalLMBatch,
+    }
 except (ImportError, NotImplementedError):
     # These imports can fail on CPU/Non flash.
     VLM_BATCH_TYPES = set()
@@ -199,6 +205,7 @@ def serve(
     quantize: Optional[str],
     speculate: Optional[int],
     dtype: Optional[str],
+    kv_cache_dtype: Optional[str],
     trust_remote_code: bool,
     uds_path: Path,
     max_input_tokens: int,
@@ -211,6 +218,7 @@ def serve(
         quantize: Optional[str] = None,
         speculate: Optional[int] = None,
         dtype: Optional[str] = None,
+        kv_cache_dtype: Optional[str] = None,
         trust_remote_code: bool = False,
     ):
         unix_socket_template = "unix://{}-{}"
@@ -234,6 +242,7 @@ def serve(
                 quantize,
                 speculate,
                 dtype,
+                kv_cache_dtype,
                 trust_remote_code,
                 max_input_tokens,
                 adapter_to_index,
@@ -280,6 +289,7 @@ def serve(
             quantize,
             speculate,
             dtype,
+            kv_cache_dtype,
             trust_remote_code,
         )
     )
diff --git a/server/text_generation_server/utils/import_utils.py b/server/text_generation_server/utils/import_utils.py
index 782b4f15..b693258c 100644
--- a/server/text_generation_server/utils/import_utils.py
+++ b/server/text_generation_server/utils/import_utils.py
@@ -66,6 +66,11 @@ elif is_ipex_available():
         empty_cache = noop
         synchronize = noop
         get_free_memory = get_cpu_free_memory
+elif hasattr(torch, "xpu") and torch.xpu.is_available():
+    SYSTEM = "xpu"
+    empty_cache = torch.xpu.empty_cache
+    synchronize = torch.xpu.synchronize
+    get_free_memory = get_xpu_free_memory
 else:
     SYSTEM = "cpu"
 
diff --git a/update_doc.py b/update_doc.py
index 3fb0d314..203aaced 100644
--- a/update_doc.py
+++ b/update_doc.py
@@ -5,14 +5,13 @@ import json
 import os
 
 TEMPLATE = """
-# Supported Models and Hardware
+# Supported Models
 
-Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models (VLMs & LLMs) are supported.
-
-## Supported Models
+Text Generation Inference enables serving optimized models. The following sections list which models (VLMs & LLMs) are supported.
 
 SUPPORTED_MODELS
 
+
 If the above list lacks the model you would like to serve, depending on the model's pipeline type, you can try to initialize and serve the model anyways to see how well it performs, but performance isn't guaranteed for non-optimized models:
 
 ```python