diff --git a/Cargo.lock b/Cargo.lock
index 04d42397..2b537e09 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -88,9 +88,9 @@ dependencies = [
 
 [[package]]
 name = "anyhow"
-version = "1.0.81"
+version = "1.0.82"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0952808a6c2afd1aa8947271f3a60f1a6763c7b912d210184c5149b5cf147247"
+checksum = "f538837af36e6f6a9be0faa67f9a314f8119e4e4b5867c6ab40ed60360142519"
 
 [[package]]
 name = "arc-swap"
@@ -128,18 +128,18 @@ checksum = "16e62a023e7c117e27523144c5d2459f4397fcc3cab0085af8e2224f643a0193"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
 name = "async-trait"
-version = "0.1.79"
+version = "0.1.80"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a507401cad91ec6a857ed5513a2073c82a9b9048762b885bb98655b306964681"
+checksum = "c6fa2087f2753a7da8cc1c0dbfcf89579dd57458e36769de5ac750b4671737ca"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -288,9 +288,9 @@ dependencies = [
 
 [[package]]
 name = "bumpalo"
-version = "3.15.4"
+version = "3.16.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7ff69b9dd49fd426c69a0db9fc04dd934cdb6645ff000864d98f7e2af8830eaa"
+checksum = "79296716171880943b8470b5f8d03aa55eb2e645a4874bdbb28adb49162e012c"
 
 [[package]]
 name = "bytecount"
@@ -350,9 +350,9 @@ checksum = "df8670b8c7b9dae1793364eafadf7239c40d669904660c5960d74cfd80b46a53"
 
 [[package]]
 name = "cc"
-version = "1.0.90"
+version = "1.0.94"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8cd6604a82acf3039f1144f54b8eb34e91ffba622051189e71b781822d5ee1f5"
+checksum = "17f6e324229dc011159fcc089755d1e2e216a90d43a7dea6853ca740b84f35e7"
 
 [[package]]
 name = "cfg-if"
@@ -397,7 +397,7 @@ dependencies = [
  "heck 0.5.0",
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -675,9 +675,9 @@ dependencies = [
 
 [[package]]
 name = "either"
-version = "1.10.0"
+version = "1.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "11157ac094ffbdde99aa67b23417ebdd801842852b500e395a45a9c0aac03e4a"
+checksum = "a47c1c47d2f5964e29c61246e81db715514cd532db6b5116a25ea3c03d6780a2"
 
 [[package]]
 name = "encode_unicode"
@@ -687,9 +687,9 @@ checksum = "a357d28ed41a50f9c765dbfe56cbc04a64e53e5fc58ba79fbc34c10ef3df831f"
 
 [[package]]
 name = "encoding_rs"
-version = "0.8.33"
+version = "0.8.34"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7268b386296a025e474d5140678f75d6de9493ae55a5d709eeb9dd08149945e1"
+checksum = "b45de904aa0b010bce2ab45264d0631681847fa7b6f2eaa7dab7619943bc4f59"
 dependencies = [
  "cfg-if",
 ]
@@ -839,7 +839,7 @@ checksum = "87750cf4b7a4c0625b1529e4c543c2182106e4dedc60a2a6455e00d212c489ac"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -893,9 +893,9 @@ dependencies = [
 
 [[package]]
 name = "getrandom"
-version = "0.2.12"
+version = "0.2.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "190092ea657667030ac6a35e305e62fc4dd69fd98ac98631e5d3a2b1575a12b5"
+checksum = "94b22e06ecb0110981051723910cbf0b5f5e09a2062dd7663334ee79a9d1286c"
 dependencies = [
  "cfg-if",
  "libc",
@@ -920,9 +920,9 @@ dependencies = [
 
 [[package]]
 name = "h2"
-version = "0.3.25"
+version = "0.3.26"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4fbd2820c5e49886948654ab546d0688ff24530286bdcf8fca3cefb16d4618eb"
+checksum = "81fe527a889e1532da5c525686d96d4c2e74cdd345badf8dfef9f6b39dd5f5e8"
 dependencies = [
  "bytes",
  "fnv",
@@ -993,15 +993,6 @@ dependencies = [
  "ureq",
 ]
 
-[[package]]
-name = "home"
-version = "0.5.9"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e3d1354bf6b7235cb4a0576c2619fd4ed18183f689b12b006a0ee7329eeff9a5"
-dependencies = [
- "windows-sys 0.52.0",
-]
-
 [[package]]
 name = "hostname"
 version = "0.3.1"
@@ -1204,6 +1195,15 @@ dependencies = [
  "either",
 ]
 
+[[package]]
+name = "itertools"
+version = "0.12.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ba291022dbbd398a455acf126c1e341954079855bc60dfdda641363bd6922569"
+dependencies = [
+ "either",
+]
+
 [[package]]
 name = "itoa"
 version = "1.0.11"
@@ -1358,7 +1358,7 @@ checksum = "38b4faf00617defe497754acde3024865bc143d44a86799b24e191ecff91354f"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -1421,9 +1421,9 @@ dependencies = [
 
 [[package]]
 name = "monostate"
-version = "0.1.11"
+version = "0.1.12"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "878c2a1f1c70e5724fa28f101ca787b6a7e8ad5c5e4ae4ca3b0fa4a419fa9075"
+checksum = "a20fffcd8ca4c69d31e036a71abc400147b41f90895df4edcb36497a1f8af8bf"
 dependencies = [
  "monostate-impl",
  "serde",
@@ -1431,20 +1431,20 @@ dependencies = [
 
 [[package]]
 name = "monostate-impl"
-version = "0.1.11"
+version = "0.1.12"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f686d68a09079e63b1d2c64aa305095887ce50565f00a922ebfaeeee0d9ba6ce"
+checksum = "bf307cbbbd777a9c10cec88ddafee572b3484caad5cce0c9236523c3803105a6"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
 name = "multimap"
-version = "0.8.3"
+version = "0.10.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e5ce46fe64a9d73be07dcbe690a38ce1b293be448fd8ce1e6c1b8062c9f72c6a"
+checksum = "defc4c55412d89136f966bbb339008b474350e5e6e78d2714439c386b3137a03"
 
 [[package]]
 name = "muxado"
@@ -1662,7 +1662,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -1878,7 +1878,7 @@ checksum = "2f38a4412a78282e09a2cf38d195ea5420d15ba0602cb375210efbc877243965"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -1919,12 +1919,12 @@ checksum = "5b40af805b3121feab8a3c29f04d8ad262fa8e0561883e7653e024ae4479e6de"
 
 [[package]]
 name = "prettyplease"
-version = "0.2.17"
+version = "0.2.19"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8d3928fb5db768cb86f891ff014f0144589297e3c6a1aba6ed7cecfdace270c7"
+checksum = "5ac2cf0f2e4f42b49f5ffd07dae8d746508ef7526c13940e5f524012ae6c6550"
 dependencies = [
  "proc-macro2",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -1953,9 +1953,9 @@ dependencies = [
 
 [[package]]
 name = "proc-macro2"
-version = "1.0.79"
+version = "1.0.81"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e835ff2298f5721608eb1a980ecaee1aef2c132bf95ecc026a11b7bf3c01c02e"
+checksum = "3d1597b0c024618f09a9c3b8655b7e430397a36d23fdafec26d6965e9eec3eba"
 dependencies = [
  "unicode-ident",
 ]
@@ -1972,34 +1972,33 @@ dependencies = [
 
 [[package]]
 name = "prost"
-version = "0.12.3"
+version = "0.12.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "146c289cda302b98a28d40c8b3b90498d6e526dd24ac2ecea73e4e491685b94a"
+checksum = "d0f5d036824e4761737860779c906171497f6d55681139d8312388f8fe398922"
 dependencies = [
  "bytes",
- "prost-derive 0.12.3",
+ "prost-derive 0.12.4",
 ]
 
 [[package]]
 name = "prost-build"
-version = "0.12.3"
+version = "0.12.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c55e02e35260070b6f716a2423c2ff1c3bb1642ddca6f99e1f26d06268a0e2d2"
+checksum = "80b776a1b2dc779f5ee0641f8ade0125bc1298dd41a9a0c16d8bd57b42d222b1"
 dependencies = [
  "bytes",
- "heck 0.4.1",
- "itertools 0.11.0",
+ "heck 0.5.0",
+ "itertools 0.12.1",
  "log",
  "multimap",
  "once_cell",
  "petgraph",
  "prettyplease",
- "prost 0.12.3",
+ "prost 0.12.4",
  "prost-types",
  "regex",
- "syn 2.0.58",
+ "syn 2.0.60",
  "tempfile",
- "which",
 ]
 
 [[package]]
@@ -2017,24 +2016,24 @@ dependencies = [
 
 [[package]]
 name = "prost-derive"
-version = "0.12.3"
+version = "0.12.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "efb6c9a1dd1def8e2124d17e83a20af56f1570d6c2d2bd9e266ccb768df3840e"
+checksum = "19de2de2a00075bf566bee3bd4db014b11587e84184d3f7a791bc17f1a8e9e48"
 dependencies = [
  "anyhow",
- "itertools 0.11.0",
+ "itertools 0.12.1",
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
 name = "prost-types"
-version = "0.12.3"
+version = "0.12.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "193898f59edcf43c26227dcd4c8427f00d99d61e95dcde58dabd49fa291d470e"
+checksum = "3235c33eb02c1f1e212abdbe34c78b264b038fb58ca612664343271e36e55ffe"
 dependencies = [
- "prost 0.12.3",
+ "prost 0.12.4",
 ]
 
 [[package]]
@@ -2055,9 +2054,9 @@ dependencies = [
 
 [[package]]
 name = "quote"
-version = "1.0.35"
+version = "1.0.36"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "291ec9ab5efd934aaf503a6466c5d5251535d108ee747472c3977cc5acc868ef"
+checksum = "0fa76aaf39101c457836aec0ce2316dbdc3ab723cdda1c6bd4e6ad4208acaca7"
 dependencies = [
  "proc-macro2",
 ]
@@ -2310,7 +2309,7 @@ dependencies = [
  "quote",
  "rust-embed-utils",
  "shellexpand",
- "syn 2.0.58",
+ "syn 2.0.60",
  "walkdir",
 ]
 
@@ -2406,9 +2405,9 @@ dependencies = [
 
 [[package]]
 name = "rustversion"
-version = "1.0.14"
+version = "1.0.15"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7ffc183a10b4478d04cbbbfc96d0873219d962dd5accaff2ffbd4ceb7df837f4"
+checksum = "80af6f9131f277a45a3fba6ce8e2258037bb0477a67e610d3c1fe046ab31de47"
 
 [[package]]
 name = "ryu"
@@ -2484,29 +2483,29 @@ dependencies = [
 
 [[package]]
 name = "serde"
-version = "1.0.197"
+version = "1.0.198"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3fb1c873e1b9b056a4dc4c0c198b24c3ffa059243875552b2bd0933b1aee4ce2"
+checksum = "9846a40c979031340571da2545a4e5b7c4163bdae79b301d5f86d03979451fcc"
 dependencies = [
  "serde_derive",
 ]
 
 [[package]]
 name = "serde_derive"
-version = "1.0.197"
+version = "1.0.198"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7eb0b34b42edc17f6b7cac84a52a1c5f0e1bb2227e997ca9011ea3dd34e8610b"
+checksum = "e88edab869b01783ba905e7d0153f9fc1a6505a96e4ad3018011eedb838566d9"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
 name = "serde_json"
-version = "1.0.115"
+version = "1.0.116"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "12dc5c46daa8e9fdf4f5e71b6cf9a53f2487da0e86e55808e2d35539666497dd"
+checksum = "3e17db7126d17feb94eb3fad46bf1a96b034e8aacbc2e775fe81505f8b0b2813"
 dependencies = [
  "itoa",
  "ryu",
@@ -2689,7 +2688,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustversion",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -2711,9 +2710,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.58"
+version = "2.0.60"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "44cfb93f38070beee36b3fef7d4f5a16f27751d94b187b666a5cc5e9b0d30687"
+checksum = "909518bc7b1c9b779f1bbf07f2929d35af9f0f37e47c6e9ef7f9dddc1e1821f3"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -2728,9 +2727,9 @@ checksum = "2047c6ded9c721764247e62cd3b03c09ffc529b2ba5b10ec482ae507a4a70160"
 
 [[package]]
 name = "sysinfo"
-version = "0.30.8"
+version = "0.30.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4b1a378e48fb3ce3a5cf04359c456c9c98ff689bcf1c1bc6e6a31f247686f275"
+checksum = "26d7c217777061d5a2d652aea771fb9ba98b6dade657204b08c4b9604d11555b"
 dependencies = [
  "cfg-if",
  "core-foundation-sys",
@@ -2824,7 +2823,7 @@ version = "1.2.0"
 dependencies = [
  "futures",
  "grpc-metadata",
- "prost 0.12.3",
+ "prost 0.12.4",
  "prost-build",
  "rand",
  "thiserror",
@@ -2903,7 +2902,7 @@ checksum = "c61f3ba182994efc43764a46c018c347bc492c79f024e705f46567b418f6d4f7"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -2918,9 +2917,9 @@ dependencies = [
 
 [[package]]
 name = "time"
-version = "0.3.34"
+version = "0.3.36"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c8248b6521bb14bc45b4067159b9b6ad792e2d6d754d6c41fb50e29fefe38749"
+checksum = "5dfd88e563464686c916c7e46e623e520ddc6d79fa6641390f2e3fa86e83e885"
 dependencies = [
  "deranged",
  "itoa",
@@ -2941,9 +2940,9 @@ checksum = "ef927ca75afb808a4d64dd374f00a2adf8d0fcff8e7b184af886c3c87ec4a3f3"
 
 [[package]]
 name = "time-macros"
-version = "0.2.17"
+version = "0.2.18"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7ba3a3ef41e6672a2f0f001392bb5dcd3ff0a9992d618ca761a11c3121547774"
+checksum = "3f252a68540fde3a3877aeea552b832b40ab9a69e318efd078774a01ddee1ccf"
 dependencies = [
  "num-conv",
  "time-core",
@@ -3035,7 +3034,7 @@ checksum = "5b8a1e28f2deaa14e508979454cb3a223b10b938b45af148bc0986de36f1923b"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -3131,7 +3130,7 @@ dependencies = [
  "hyper-timeout",
  "percent-encoding",
  "pin-project",
- "prost 0.12.3",
+ "prost 0.12.4",
  "tokio",
  "tokio-stream",
  "tower",
@@ -3150,7 +3149,7 @@ dependencies = [
  "proc-macro2",
  "prost-build",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -3223,7 +3222,7 @@ checksum = "34704c8d6ebcbc939824180af020566b01a7c01f80641264eba0999f6c2b6be7"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -3464,7 +3463,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "regex",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
@@ -3563,7 +3562,7 @@ dependencies = [
  "once_cell",
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
  "wasm-bindgen-shared",
 ]
 
@@ -3597,7 +3596,7 @@ checksum = "e94f17b526d0a461a191c78ea52bbce64071ed5c04c9ffe424dcb38f74171bb7"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
  "wasm-bindgen-backend",
  "wasm-bindgen-shared",
 ]
@@ -3637,18 +3636,6 @@ dependencies = [
  "rustls-pki-types",
 ]
 
-[[package]]
-name = "which"
-version = "4.4.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "87ba24419a2078cd2b0f2ede2691b6c66d8e47836da3b6db8265ebad47afbfc7"
-dependencies = [
- "either",
- "home",
- "once_cell",
- "rustix",
-]
-
 [[package]]
 name = "winapi"
 version = "0.3.9"
@@ -3687,7 +3674,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e48a53791691ab099e5e2ad123536d0fff50652600abaf43bbf952894110d0be"
 dependencies = [
  "windows-core",
- "windows-targets 0.52.4",
+ "windows-targets 0.52.5",
 ]
 
 [[package]]
@@ -3696,7 +3683,7 @@ version = "0.52.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "33ab640c8d7e35bf8ba19b884ba838ceb4fba93a4e8c65a9059d08afcfc683d9"
 dependencies = [
- "windows-targets 0.52.4",
+ "windows-targets 0.52.5",
 ]
 
 [[package]]
@@ -3723,7 +3710,7 @@ version = "0.52.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "282be5f36a8ce781fad8c8ae18fa3f9beff57ec1b52cb3de0789201425d9a33d"
 dependencies = [
- "windows-targets 0.52.4",
+ "windows-targets 0.52.5",
 ]
 
 [[package]]
@@ -3758,17 +3745,18 @@ dependencies = [
 
 [[package]]
 name = "windows-targets"
-version = "0.52.4"
+version = "0.52.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7dd37b7e5ab9018759f893a1952c9420d060016fc19a472b4bb20d1bdd694d1b"
+checksum = "6f0713a46559409d202e70e28227288446bf7841d3211583a4b53e3f6d96e7eb"
 dependencies = [
- "windows_aarch64_gnullvm 0.52.4",
- "windows_aarch64_msvc 0.52.4",
- "windows_i686_gnu 0.52.4",
- "windows_i686_msvc 0.52.4",
- "windows_x86_64_gnu 0.52.4",
- "windows_x86_64_gnullvm 0.52.4",
- "windows_x86_64_msvc 0.52.4",
+ "windows_aarch64_gnullvm 0.52.5",
+ "windows_aarch64_msvc 0.52.5",
+ "windows_i686_gnu 0.52.5",
+ "windows_i686_gnullvm",
+ "windows_i686_msvc 0.52.5",
+ "windows_x86_64_gnu 0.52.5",
+ "windows_x86_64_gnullvm 0.52.5",
+ "windows_x86_64_msvc 0.52.5",
 ]
 
 [[package]]
@@ -3785,9 +3773,9 @@ checksum = "2b38e32f0abccf9987a4e3079dfb67dcd799fb61361e53e2882c3cbaf0d905d8"
 
 [[package]]
 name = "windows_aarch64_gnullvm"
-version = "0.52.4"
+version = "0.52.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bcf46cf4c365c6f2d1cc93ce535f2c8b244591df96ceee75d8e83deb70a9cac9"
+checksum = "7088eed71e8b8dda258ecc8bac5fb1153c5cffaf2578fc8ff5d61e23578d3263"
 
 [[package]]
 name = "windows_aarch64_msvc"
@@ -3803,9 +3791,9 @@ checksum = "dc35310971f3b2dbbf3f0690a219f40e2d9afcf64f9ab7cc1be722937c26b4bc"
 
 [[package]]
 name = "windows_aarch64_msvc"
-version = "0.52.4"
+version = "0.52.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "da9f259dd3bcf6990b55bffd094c4f7235817ba4ceebde8e6d11cd0c5633b675"
+checksum = "9985fd1504e250c615ca5f281c3f7a6da76213ebd5ccc9561496568a2752afb6"
 
 [[package]]
 name = "windows_i686_gnu"
@@ -3821,9 +3809,15 @@ checksum = "a75915e7def60c94dcef72200b9a8e58e5091744960da64ec734a6c6e9b3743e"
 
 [[package]]
 name = "windows_i686_gnu"
-version = "0.52.4"
+version = "0.52.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b474d8268f99e0995f25b9f095bc7434632601028cf86590aea5c8a5cb7801d3"
+checksum = "88ba073cf16d5372720ec942a8ccbf61626074c6d4dd2e745299726ce8b89670"
+
+[[package]]
+name = "windows_i686_gnullvm"
+version = "0.52.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "87f4261229030a858f36b459e748ae97545d6f1ec60e5e0d6a3d32e0dc232ee9"
 
 [[package]]
 name = "windows_i686_msvc"
@@ -3839,9 +3833,9 @@ checksum = "8f55c233f70c4b27f66c523580f78f1004e8b5a8b659e05a4eb49d4166cca406"
 
 [[package]]
 name = "windows_i686_msvc"
-version = "0.52.4"
+version = "0.52.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1515e9a29e5bed743cb4415a9ecf5dfca648ce85ee42e15873c3cd8610ff8e02"
+checksum = "db3c2bf3d13d5b658be73463284eaf12830ac9a26a90c717b7f771dfe97487bf"
 
 [[package]]
 name = "windows_x86_64_gnu"
@@ -3857,9 +3851,9 @@ checksum = "53d40abd2583d23e4718fddf1ebec84dbff8381c07cae67ff7768bbf19c6718e"
 
 [[package]]
 name = "windows_x86_64_gnu"
-version = "0.52.4"
+version = "0.52.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5eee091590e89cc02ad514ffe3ead9eb6b660aedca2183455434b93546371a03"
+checksum = "4e4246f76bdeff09eb48875a0fd3e2af6aada79d409d33011886d3e1581517d9"
 
 [[package]]
 name = "windows_x86_64_gnullvm"
@@ -3875,9 +3869,9 @@ checksum = "0b7b52767868a23d5bab768e390dc5f5c55825b6d30b86c844ff2dc7414044cc"
 
 [[package]]
 name = "windows_x86_64_gnullvm"
-version = "0.52.4"
+version = "0.52.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "77ca79f2451b49fa9e2af39f0747fe999fcda4f5e241b2898624dca97a1f2177"
+checksum = "852298e482cd67c356ddd9570386e2862b5673c85bd5f88df9ab6802b334c596"
 
 [[package]]
 name = "windows_x86_64_msvc"
@@ -3893,9 +3887,9 @@ checksum = "ed94fce61571a4006852b7389a063ab983c02eb1bb37b47f8272ce92d06d9538"
 
 [[package]]
 name = "windows_x86_64_msvc"
-version = "0.52.4"
+version = "0.52.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "32b752e52a2da0ddfbdbcc6fceadfeede4c939ed16d13e648833a61dfb611ed8"
+checksum = "bec47e5bfd1bff0eeaf6d8b485cc1074891a197ab4225d504cb7a1ab88b02bf0"
 
 [[package]]
 name = "winreg"
@@ -3924,7 +3918,7 @@ checksum = "9ce1b18ccd8e73a9321186f97e46f9f04b778851177567b1975109d26a08d2a6"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.58",
+ "syn 2.0.60",
 ]
 
 [[package]]
diff --git a/docs/source/basic_tutorials/launcher.md b/docs/source/basic_tutorials/launcher.md
index 62abe8c6..9590e463 100644
--- a/docs/source/basic_tutorials/launcher.md
+++ b/docs/source/basic_tutorials/launcher.md
@@ -67,6 +67,14 @@ Options:
           - bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16
           - bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
 
+```
+## SPECULATE
+```shell
+      --speculate <SPECULATE>
+          The number of input_ids to speculate on If using a medusa model, the heads will be picked up automatically Other wise, it will use n-gram speculation which is relatively free in terms of compute, but the speedup heavily depends on the task
+          
+          [env: SPECULATE=]
+
 ```
 ## DTYPE
 ```shell
diff --git a/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_all_params.json b/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_all_params.json
new file mode 100644
index 00000000..d8a298eb
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_all_params.json
@@ -0,0 +1,98 @@
+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 10,
+    "prefill": [
+      {
+        "id": 1,
+        "logprob": null,
+        "text": "<s>"
+      },
+      {
+        "id": 338,
+        "logprob": -10.0078125,
+        "text": "is"
+      },
+      {
+        "id": 21784,
+        "logprob": -15.515625,
+        "text": "Deep"
+      },
+      {
+        "id": 29257,
+        "logprob": -2.8847656,
+        "text": "Learning"
+      },
+      {
+        "id": 29973,
+        "logprob": -4.140625,
+        "text": "?"
+      }
+    ],
+    "seed": 0,
+    "tokens": [
+      {
+        "id": 13,
+        "logprob": -1.1582031,
+        "special": false,
+        "text": "\n"
+      },
+      {
+        "id": 2772,
+        "logprob": -0.23083496,
+        "special": false,
+        "text": "De"
+      },
+      {
+        "id": 1022,
+        "logprob": 0.0,
+        "special": false,
+        "text": "ep"
+      },
+      {
+        "id": 6509,
+        "logprob": 0.0,
+        "special": false,
+        "text": " learning"
+      },
+      {
+        "id": 29892,
+        "logprob": -0.61816406,
+        "special": false,
+        "text": ","
+      },
+      {
+        "id": 607,
+        "logprob": -0.7089844,
+        "special": false,
+        "text": " which"
+      },
+      {
+        "id": 508,
+        "logprob": -1.7724609,
+        "special": false,
+        "text": " can"
+      },
+      {
+        "id": 367,
+        "logprob": 0.0,
+        "special": false,
+        "text": " be"
+      },
+      {
+        "id": 5545,
+        "logprob": 0.0,
+        "special": false,
+        "text": " considered"
+      },
+      {
+        "id": 408,
+        "logprob": -0.3869629,
+        "special": false,
+        "text": " as"
+      }
+    ]
+  },
+  "generated_text": "What is Deep Learning?\nDeep learning, which can be considered as"
+}
diff --git a/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_load.json b/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_load.json
new file mode 100644
index 00000000..413af1d7
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_load.json
@@ -0,0 +1,414 @@
+[
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1,
+          "logprob": null,
+          "text": "<s>"
+        },
+        {
+          "id": 1724,
+          "logprob": -10.734375,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -1.5488281,
+          "text": "is"
+        },
+        {
+          "id": 21784,
+          "logprob": -9.2890625,
+          "text": "Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -1.2753906,
+          "text": "Learning"
+        },
+        {
+          "id": 29973,
+          "logprob": -0.48046875,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.1845703,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2772,
+          "logprob": -0.5727539,
+          "special": false,
+          "text": "De"
+        },
+        {
+          "id": 1022,
+          "logprob": -0.00010967255,
+          "special": false,
+          "text": "ep"
+        },
+        {
+          "id": 6509,
+          "logprob": -0.1239624,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 338,
+          "logprob": -0.04510498,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 263,
+          "logprob": -0.018295288,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 11306,
+          "logprob": -0.45922852,
+          "special": false,
+          "text": " subset"
+        },
+        {
+          "id": 310,
+          "logprob": -0.00020992756,
+          "special": false,
+          "text": " of"
+        },
+        {
+          "id": 4933,
+          "logprob": -0.0046539307,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 6509,
+          "logprob": -0.00025844574,
+          "special": false,
+          "text": " learning"
+        }
+      ]
+    },
+    "generated_text": "\nDeep learning is a subset of machine learning"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1,
+          "logprob": null,
+          "text": "<s>"
+        },
+        {
+          "id": 1724,
+          "logprob": -10.734375,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -1.5488281,
+          "text": "is"
+        },
+        {
+          "id": 21784,
+          "logprob": -9.2890625,
+          "text": "Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -1.2724609,
+          "text": "Learning"
+        },
+        {
+          "id": 29973,
+          "logprob": -0.47729492,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.1826172,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2772,
+          "logprob": -0.56689453,
+          "special": false,
+          "text": "De"
+        },
+        {
+          "id": 1022,
+          "logprob": -0.000108003616,
+          "special": false,
+          "text": "ep"
+        },
+        {
+          "id": 6509,
+          "logprob": -0.1239624,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 338,
+          "logprob": -0.044433594,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 263,
+          "logprob": -0.018295288,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 11306,
+          "logprob": -0.45922852,
+          "special": false,
+          "text": " subset"
+        },
+        {
+          "id": 310,
+          "logprob": -0.0002104044,
+          "special": false,
+          "text": " of"
+        },
+        {
+          "id": 4933,
+          "logprob": -0.004711151,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 6509,
+          "logprob": -0.00025892258,
+          "special": false,
+          "text": " learning"
+        }
+      ]
+    },
+    "generated_text": "\nDeep learning is a subset of machine learning"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1,
+          "logprob": null,
+          "text": "<s>"
+        },
+        {
+          "id": 1724,
+          "logprob": -10.734375,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -1.5488281,
+          "text": "is"
+        },
+        {
+          "id": 21784,
+          "logprob": -9.2890625,
+          "text": "Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -1.2724609,
+          "text": "Learning"
+        },
+        {
+          "id": 29973,
+          "logprob": -0.47729492,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.1826172,
+          "special": false,
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+          "logprob": -0.56689453,
+          "special": false,
+          "text": "De"
+        },
+        {
+          "id": 1022,
+          "logprob": -0.000108003616,
+          "special": false,
+          "text": "ep"
+        },
+        {
+          "id": 6509,
+          "logprob": -0.1239624,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 338,
+          "logprob": -0.044433594,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 263,
+          "logprob": -0.018295288,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 11306,
+          "logprob": -0.45922852,
+          "special": false,
+          "text": " subset"
+        },
+        {
+          "id": 310,
+          "logprob": -0.0002104044,
+          "special": false,
+          "text": " of"
+        },
+        {
+          "id": 4933,
+          "logprob": -0.004711151,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 6509,
+          "logprob": -0.00025892258,
+          "special": false,
+          "text": " learning"
+        }
+      ]
+    },
+    "generated_text": "\nDeep learning is a subset of machine learning"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1,
+          "logprob": null,
+          "text": "<s>"
+        },
+        {
+          "id": 1724,
+          "logprob": -10.734375,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -1.5488281,
+          "text": "is"
+        },
+        {
+          "id": 21784,
+          "logprob": -9.2890625,
+          "text": "Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -1.2724609,
+          "text": "Learning"
+        },
+        {
+          "id": 29973,
+          "logprob": -0.47729492,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.1826172,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2772,
+          "logprob": -0.56689453,
+          "special": false,
+          "text": "De"
+        },
+        {
+          "id": 1022,
+          "logprob": -0.000108003616,
+          "special": false,
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+        {
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+          "text": " learning"
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+          "special": false,
+          "text": " is"
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+          "id": 263,
+          "logprob": -0.018295288,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 11306,
+          "logprob": -0.45922852,
+          "special": false,
+          "text": " subset"
+        },
+        {
+          "id": 310,
+          "logprob": -0.0002104044,
+          "special": false,
+          "text": " of"
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+        {
+          "id": 4933,
+          "logprob": -0.004711151,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 6509,
+          "logprob": -0.00025892258,
+          "special": false,
+          "text": " learning"
+        }
+      ]
+    },
+    "generated_text": "\nDeep learning is a subset of machine learning"
+  }
+]
diff --git a/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_simple.json b/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_simple.json
new file mode 100644
index 00000000..15754b14
--- /dev/null
+++ b/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_simple.json
@@ -0,0 +1,103 @@
+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 10,
+    "prefill": [
+      {
+        "id": 1,
+        "logprob": null,
+        "text": "<s>"
+      },
+      {
+        "id": 1724,
+        "logprob": -10.734375,
+        "text": "What"
+      },
+      {
+        "id": 338,
+        "logprob": -1.5488281,
+        "text": "is"
+      },
+      {
+        "id": 21784,
+        "logprob": -9.2890625,
+        "text": "Deep"
+      },
+      {
+        "id": 29257,
+        "logprob": -1.2753906,
+        "text": "Learning"
+      },
+      {
+        "id": 29973,
+        "logprob": -0.48046875,
+        "text": "?"
+      }
+    ],
+    "seed": null,
+    "tokens": [
+      {
+        "id": 13,
+        "logprob": -1.1845703,
+        "special": false,
+        "text": "\n"
+      },
+      {
+        "id": 2772,
+        "logprob": -0.5727539,
+        "special": false,
+        "text": "De"
+      },
+      {
+        "id": 1022,
+        "logprob": -0.000108122826,
+        "special": false,
+        "text": "ep"
+      },
+      {
+        "id": 6509,
+        "logprob": -0.1239624,
+        "special": false,
+        "text": " learning"
+      },
+      {
+        "id": 338,
+        "logprob": -0.044433594,
+        "special": false,
+        "text": " is"
+      },
+      {
+        "id": 263,
+        "logprob": -0.01852417,
+        "special": false,
+        "text": " a"
+      },
+      {
+        "id": 11306,
+        "logprob": -0.45922852,
+        "special": false,
+        "text": " subset"
+      },
+      {
+        "id": 310,
+        "logprob": -0.0002104044,
+        "special": false,
+        "text": " of"
+      },
+      {
+        "id": 4933,
+        "logprob": -0.004787445,
+        "special": false,
+        "text": " machine"
+      },
+      {
+        "id": 6509,
+        "logprob": -0.00026226044,
+        "special": false,
+        "text": " learning"
+      }
+    ]
+  },
+  "generated_text": "\nDeep learning is a subset of machine learning"
+}
diff --git a/integration-tests/models/test_flash_medusa.py b/integration-tests/models/test_flash_medusa.py
new file mode 100644
index 00000000..003409b0
--- /dev/null
+++ b/integration-tests/models/test_flash_medusa.py
@@ -0,0 +1,59 @@
+import pytest
+
+
+@pytest.fixture(scope="module")
+def flash_medusa_handle(launcher):
+    with launcher("FasterDecoding/medusa-vicuna-7b-v1.3", num_shard=2) as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def flash_medusa(flash_medusa_handle):
+    await flash_medusa_handle.health(300)
+    return flash_medusa_handle.client
+
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_medusa_simple(flash_medusa, response_snapshot):
+    response = await flash_medusa.generate(
+        "What is Deep Learning?", max_new_tokens=10, decoder_input_details=True
+    )
+
+    assert response.details.generated_tokens == 10
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_medusa_all_params(flash_medusa, response_snapshot):
+    response = await flash_medusa.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 == response_snapshot
+
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_medusa_load(flash_medusa, generate_load, response_snapshot):
+    responses = await generate_load(flash_medusa, "What is Deep Learning?", 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[0].generated_text == '\nDeep learning is a subset of machine learning' 
+
+    assert responses == response_snapshot
diff --git a/integration-tests/models/test_flash_mistral.py b/integration-tests/models/test_flash_mistral.py
index 63cb09b5..7d21afd9 100644
--- a/integration-tests/models/test_flash_mistral.py
+++ b/integration-tests/models/test_flash_mistral.py
@@ -21,6 +21,7 @@ async def test_flash_mistral(flash_mistral, response_snapshot):
     )
 
     assert response.details.generated_tokens == 10
+    assert response.generated_text == ": Let n = 10 - 1"
     assert response == response_snapshot
 
 
@@ -55,6 +56,7 @@ async def test_flash_mistral_load(flash_mistral, generate_load, response_snapsho
     )
 
     assert len(responses) == 4
-    assert all([r.generated_text == responses[0].generated_text for r in responses])
+    assert all([r.generated_text == responses[0].generated_text for r in responses]), f"{[r.generated_text  for r in responses]}"
+    assert responses[0].generated_text == ": Let n = 10 - 1"
 
     assert responses == response_snapshot
diff --git a/launcher/src/main.rs b/launcher/src/main.rs
index 0a023234..e814b833 100644
--- a/launcher/src/main.rs
+++ b/launcher/src/main.rs
@@ -157,6 +157,13 @@ struct Args {
     #[clap(long, env, value_enum)]
     quantize: Option<Quantization>,
 
+    /// The number of input_ids to speculate on
+    /// If using a medusa model, the heads will be picked up automatically
+    /// Other wise, it will use n-gram speculation which is relatively free
+    /// in terms of compute, but the speedup heavily depends on the task.
+    #[clap(long, env)]
+    speculate: Option<usize>,
+
     /// The dtype to be forced upon the model. This option cannot be used with `--quantize`.
     #[clap(long, env, value_enum)]
     dtype: Option<Dtype>,
@@ -377,6 +384,7 @@ fn shard_manager(
     model_id: String,
     revision: Option<String>,
     quantize: Option<Quantization>,
+    speculate: Option<usize>,
     dtype: Option<Dtype>,
     max_total_tokens: usize,
     trust_remote_code: bool,
@@ -435,6 +443,11 @@ fn shard_manager(
         shard_args.push(quantize.to_string())
     }
 
+    if let Some(speculate) = speculate {
+        shard_args.push("--speculate".to_string());
+        shard_args.push(speculate.to_string())
+    }
+
     if let Some(dtype) = dtype {
         shard_args.push("--dtype".to_string());
         shard_args.push(dtype.to_string())
@@ -890,6 +903,7 @@ fn spawn_shards(
         let shutdown_sender = shutdown_sender.clone();
         let otlp_endpoint = args.otlp_endpoint.clone();
         let quantize = args.quantize;
+        let speculate = args.speculate;
         let dtype = args.dtype;
         let max_total_tokens = args.max_total_tokens;
         let trust_remote_code = args.trust_remote_code;
@@ -905,6 +919,7 @@ fn spawn_shards(
                 model_id,
                 revision,
                 quantize,
+                speculate,
                 dtype,
                 max_total_tokens,
                 trust_remote_code,
diff --git a/load_tests/common.js b/load_tests/common.js
index be812e9b..5d71abea 100644
--- a/load_tests/common.js
+++ b/load_tests/common.js
@@ -7,7 +7,9 @@ const seed = 0;
 
 const host = __ENV.HOST || '127.0.0.1:8000';
 const timePerToken = new Trend('time_per_token', true);
-const throughput = new Counter('tokens_per_s');
+const tokens = new Counter('tokens');
+const new_tokens = new Counter('new_tokens');
+const input_tokens = new Counter('input_tokens');
 
 randomSeed(seed);
 // const shareGPT = JSON.parse(open("ShareGPT_V3_unfiltered_cleaned_split.json"))
@@ -19,7 +21,7 @@ export function get_options(reference_latency_ms){
         thresholds: {
             http_req_failed: ['rate==0'],
             time_per_token: [{
-                threshold: `p(50)<${3 * reference_latency_ms}`,
+                threshold: `p(50)<${5 * reference_latency_ms}`,
                 abortOnFail: true,
                 delayAbortEval: '10s'
             }],
@@ -28,7 +30,7 @@ export function get_options(reference_latency_ms){
             load_test: {
                 executor: 'constant-arrival-rate',
                 duration: '60s',
-                preAllocatedVUs: 100,
+                preAllocatedVUs: 10,
                 rate: 10,
                 timeUnit: '1s',
             },
@@ -48,17 +50,22 @@ export function run(host, generate_payload, max_new_tokens) {
         return;
     }
 
+
     check(res, {
         'Post status is 200': (r) => res.status === 200,
     });
-    const n_tokens = max_new_tokens;
-    const timings = res.timings.duration;
+    const duration = res.timings.duration;
 
     if (res.status === 200) {
-        const latency_ms_per_token = timings / n_tokens;
+        const body = res.json(); 
+        const n_tokens = body.details.tokens.length;
+        const latency_ms_per_token = duration / n_tokens;
         timePerToken.add(latency_ms_per_token);
         const latency_in_s = latency_ms_per_token / 1000;
         const individual_throughput = 1 / latency_in_s;
-        throughput.add(individual_throughput);
+        const _input_tokens = body.details.prefill.length;
+        tokens.add(n_tokens + _input_tokens);
+        input_tokens.add(_input_tokens);
+        new_tokens.add(n_tokens);
     }
 }
diff --git a/load_tests/tgi.js b/load_tests/tgi.js
index 93a0e278..1db4ab6f 100644
--- a/load_tests/tgi.js
+++ b/load_tests/tgi.js
@@ -1,13 +1,13 @@
 import { get_options, run } from "./common.js";
  
-const reference_latency_ms = 30;
+const reference_latency_ms = 70;
 const host = __ENV.HOST || '127.0.0.1:8000';
 const max_new_tokens = 50;
 
 
 function generate_payload(gpt){
     const input = gpt["conversations"][0]["value"];
-    return {"inputs": input, "parameters": {"max_new_tokens": max_new_tokens, "temperature" : 0.5}}
+    return {"inputs": input, "parameters": {"max_new_tokens": max_new_tokens, "decoder_input_details": true}}
 }
 
 export const options = get_options(reference_latency_ms);
diff --git a/proto/generate.proto b/proto/generate.proto
index c7f9f3c1..0041b907 100644
--- a/proto/generate.proto
+++ b/proto/generate.proto
@@ -1,6 +1,6 @@
 syntax = "proto3";
 
-package generate.v1;
+package generate.v2;
 
 service TextGenerationService {
     /// Model Info
@@ -32,6 +32,7 @@ message InfoResponse {
     string dtype = 2;
     string device_type = 3;
     optional uint32 window_size = 4;
+    uint32 speculate = 5;
 }
 
 /// Empty request
@@ -135,43 +136,27 @@ message GeneratedText {
     optional uint64 seed = 4;
 }
 
-message PrefillTokens {
-    /// Prefill Token IDs
+message Tokens {
+    /// Token IDs
     repeated uint32 ids = 1;
-    /// Prefill Logprobs
+    /// Logprobs
     repeated float logprobs = 2;
-    /// Prefill tokens
+    /// tokens
     repeated string texts = 3;
-}
-
-message TopTokens {
-    /// Top Token IDs
-    repeated uint32 ids = 1;
-    /// Top Logprobs
-    repeated float logprobs = 2;
-    /// Top Token Texts
-    repeated string texts = 3;
-    /// If the tokens are special
-    repeated bool is_special = 6;
+    /// special
+    repeated bool is_special = 4;
 }
 
 message Generation {
     /// Request ID
     uint64 request_id = 1;
     /// Prefill tokens (optional)
-    PrefillTokens prefill_tokens = 2;
-    /// Token ID
-    uint32 token_id = 3;
-    /// Logprob
-    float token_logprob = 4;
-    /// Text
-    string token_text = 5;
-    /// Is it a special token
-    bool token_is_special = 6;
+    Tokens prefill_tokens = 2;
+    Tokens tokens = 3;
     /// Complete generated text
-    optional GeneratedText generated_text = 7;
+    optional GeneratedText generated_text = 4;
     /// Top tokens
-    TopTokens top_tokens = 8;
+    repeated Tokens top_tokens = 5;
 }
 
 message FilterBatchRequest {
diff --git a/router/client/src/client.rs b/router/client/src/client.rs
index 486e13d9..ca86f330 100644
--- a/router/client/src/client.rs
+++ b/router/client/src/client.rs
@@ -1,8 +1,8 @@
 /// Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
 
 /// Single shard Client
-use crate::pb::generate::v1::text_generation_service_client::TextGenerationServiceClient;
-use crate::pb::generate::v1::*;
+use crate::pb::generate::v2::text_generation_service_client::TextGenerationServiceClient;
+use crate::pb::generate::v2::*;
 use crate::Result;
 use std::env;
 use rand::{distributions::Uniform, Rng};
diff --git a/router/client/src/lib.rs b/router/client/src/lib.rs
index f334be21..c38b931b 100644
--- a/router/client/src/lib.rs
+++ b/router/client/src/lib.rs
@@ -6,11 +6,11 @@ mod pb;
 mod sharded_client;
 
 pub use client::Client;
-pub use pb::generate::v1::HealthResponse;
-pub use pb::generate::v1::InfoResponse as ShardInfo;
-pub use pb::generate::v1::{
+pub use pb::generate::v2::HealthResponse;
+pub use pb::generate::v2::InfoResponse as ShardInfo;
+pub use pb::generate::v2::{
     Batch, CachedBatch, FinishReason, GeneratedText, Generation, NextTokenChooserParameters,
-    PrefillTokens, Request, StoppingCriteriaParameters,
+    Request, StoppingCriteriaParameters, Tokens,
 };
 pub use sharded_client::ShardedClient;
 use thiserror::Error;
diff --git a/router/src/infer.rs b/router/src/infer.rs
index f108a1dc..c1b01211 100644
--- a/router/src/infer.rs
+++ b/router/src/infer.rs
@@ -11,7 +11,7 @@ use std::sync::{
     Arc,
 };
 use text_generation_client::{
-    Batch, CachedBatch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient,
+    Batch, CachedBatch, ClientError, GeneratedText, Generation, ShardedClient, Tokens,
 };
 use thiserror::Error;
 use tokio::sync::mpsc::error::SendError;
@@ -54,6 +54,7 @@ impl Infer {
         max_input_length: u32,
         max_total_tokens: u32,
         window_size: Option<u32>,
+        speculate: u32,
         generation_health: Arc<AtomicBool>,
     ) -> Self {
         // Infer shared state
@@ -62,7 +63,8 @@ impl Infer {
             max_input_length,
             max_total_tokens,
             16,
-            window_size
+            window_size,
+            speculate
         );
         let shared = Arc::new(Shared {
             batching_task: Notify::new(),
@@ -533,50 +535,63 @@ fn send_responses(
     }
 
     // Create last Token
-    let token = Token {
-        id: generation.token_id,
-        text: generation.token_text,
-        logprob: generation.token_logprob,
-        special: generation.token_is_special,
-    };
-
-    // generation.top_tokens
-
-    let mut top_tokens = Vec::new();
-    if let Some(top_tokens_) = generation.top_tokens {
-        top_tokens.extend(
+    let tokens_ = generation.tokens.expect("Non empty tokens in generation");
+    let n = tokens_.ids.len();
+    metrics::histogram!("tgi_request_skipped_tokens", (n - 1) as f64);
+    let mut iterator = tokens_
+        .ids
+        .into_iter()
+        .zip(tokens_.logprobs.into_iter())
+        .zip(tokens_.texts.into_iter())
+        .zip(tokens_.is_special.into_iter())
+        .enumerate()
+        .peekable();
+    while let Some((i, (((id, logprob), text), special))) = iterator.next() {
+        let token = Token {
+            id,
+            text,
+            logprob,
+            special,
+        };
+        let top_tokens = if let Some(top_tokens_) = generation.top_tokens.get(i) {
             top_tokens_
                 .ids
-                .into_iter()
-                .zip(top_tokens_.logprobs.into_iter())
-                .zip(top_tokens_.texts.into_iter())
-                .zip(top_tokens_.is_special.into_iter())
-                .map(|(((id, logprob), text), special)| Token {
+                .iter()
+                .zip(top_tokens_.logprobs.iter())
+                .zip(top_tokens_.texts.iter())
+                .zip(top_tokens_.is_special.iter())
+                .map(|(((&id, &logprob), text), &special)| Token {
                     id,
-                    text,
+                    text: text.to_string(),
                     logprob,
                     special,
-                }),
-        )
+                })
+                .collect()
+        } else {
+            vec![]
+        };
+        match (&generation.generated_text, iterator.peek()) {
+            (Some(generated_text), None) => {
+                // Generation has ended
+                stopped = true;
+                // Send message
+                entry.response_tx.send(Ok(InferStreamResponse::End {
+                    token,
+                    top_tokens,
+                    generated_text: generated_text.clone(),
+                    queued: entry.queue_time,
+                    start: entry.batch_time.unwrap(),
+                }))?;
+            }
+            _ => {
+                // Send message
+                entry
+                    .response_tx
+                    .send(Ok(InferStreamResponse::Intermediate { token, top_tokens }))?;
+            }
+        }
     }
 
-    if let Some(generated_text) = generation.generated_text {
-        // Generation has ended
-        stopped = true;
-        // Send message
-        entry.response_tx.send(Ok(InferStreamResponse::End {
-            token,
-            top_tokens,
-            generated_text,
-            queued: entry.queue_time,
-            start: entry.batch_time.unwrap(),
-        }))?;
-    } else {
-        // Send message
-        entry
-            .response_tx
-            .send(Ok(InferStreamResponse::Intermediate { token, top_tokens }))?;
-    }
     Ok(stopped)
 }
 
@@ -601,7 +616,7 @@ fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
 #[derive(Debug)]
 pub(crate) enum InferStreamResponse {
     // Optional first message
-    Prefill(PrefillTokens),
+    Prefill(Tokens),
     // Intermediate messages
     Intermediate {
         token: Token,
diff --git a/router/src/queue.rs b/router/src/queue.rs
index 6734c6a6..6227d70c 100644
--- a/router/src/queue.rs
+++ b/router/src/queue.rs
@@ -44,7 +44,8 @@ impl Queue {
         max_input_length: u32,
         max_total_tokens: u32,
         block_size: u32,
-        window_size: Option<u32>
+        window_size: Option<u32>,
+        speculate: u32,
     ) -> Self {
         // Create channel
         let (queue_sender, queue_receiver) = mpsc::unbounded_channel();
@@ -56,6 +57,7 @@ impl Queue {
             max_total_tokens,
             block_size,
             window_size,
+            speculate,
             queue_receiver,
         ));
 
@@ -106,6 +108,7 @@ async fn queue_task(
     max_total_tokens: u32,
     block_size: u32,
     window_size: Option<u32>,
+    speculate: u32,
     mut receiver: mpsc::UnboundedReceiver<QueueCommand>,
 ) {
     let mut state = State::new(
@@ -113,7 +116,8 @@ async fn queue_task(
         max_input_length,
         max_total_tokens,
         block_size,
-        window_size
+        window_size,
+        speculate
     );
 
     while let Some(cmd) = receiver.recv().await {
@@ -256,6 +260,9 @@ struct State {
 
     /// Sliding window
     window_size: Option<u32>,
+
+    /// Speculation amount
+    speculate: u32,
 }
 
 impl State {
@@ -265,6 +272,7 @@ impl State {
         max_total_tokens: u32,
         block_size: u32,
         window_size: Option<u32>,
+        speculate: u32,
     ) -> Self {
         let default_threshold: u64 = 120;
         let threshold: u64 = match env::var("QUEUE_THRESHOLD_MS") {
@@ -281,6 +289,7 @@ impl State {
             max_total_tokens,
             block_size,
             window_size,
+            speculate,
         }
     }
 
@@ -365,7 +374,7 @@ impl State {
             }
 
             if prefill_tokens > prefill_token_budget
-                || (prefill_tokens + decode_tokens) > token_budget
+                || (prefill_tokens + decode_tokens + self.speculate) > token_budget
             {
                 // Entry is over budget
                 // Add it back to the front
@@ -457,13 +466,13 @@ mod tests {
 
     fn default_queue() -> Queue {
         Queue::new(
-            true, 1, 2, 1, None
+            true, 1, 2, 1, None, 0
         )
     }
 
     fn default_state() -> State {
         State::new(
-            true, 1, 2, 1, None
+            true, 1, 2, 1, None, 0
         )
     }
 
@@ -667,6 +676,25 @@ mod tests {
         assert_eq!(batch.size, 2);
     }
 
+    #[tokio::test]
+    async fn test_queue_next_batch_token_speculate() {
+        let queue = Queue::new(true, 1, 2, 1, None, 2);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        queue.append(entry1);
+        queue.append(entry2);
+
+        // Budget of 1 is not enough
+        assert!(queue.next_batch(None, 1, 1).await.is_none());
+
+        let (entries, batch, _) = queue.next_batch(None, 6, 6).await.unwrap();
+        assert_eq!(entries.len(), 2);
+        assert!(entries.contains_key(&0));
+        assert!(entries.contains_key(&1));
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 2);
+    }
+
     #[tokio::test]
     async fn test_queue_next_batch_dropped_receiver() {
         let queue = default_queue();
diff --git a/router/src/server.rs b/router/src/server.rs
index c2eab874..1830a879 100644
--- a/router/src/server.rs
+++ b/router/src/server.rs
@@ -600,6 +600,7 @@ pub async fn run(
         max_input_length as u32,
         max_total_tokens as u32,
         shard_info.window_size,
+        shard_info.speculate,
         generation_health,
     );
 
diff --git a/server/Makefile-vllm b/server/Makefile-vllm
index ddb648ea..c9c1d520 100644
--- a/server/Makefile-vllm
+++ b/server/Makefile-vllm
@@ -1,22 +1,25 @@
-build-vllm-cuda: REPOSITORY=https://github.com/vllm-project/vllm.git
-build-vllm-cuda: VLLM_COMMIT=f8a1e39fae05ca610be8d5a78be9d40f5274e5fc
-build-vllm-cuda: BRANCH=main
-build-vllm-cuda: build-vllm
-
-build-vllm-rocm: REPOSITORY=https://github.com/fxmarty/vllm-public.git
-build-vllm-rocm: VLLM_COMMIT=ad9b7c4095ef54419a0533d254f2ad84bd2dfcae
-build-vllm-rocm: BRANCH=rotary-no-positions-split-cos-sin
-build-vllm-rocm: build-vllm
-
-vllm:
+vllm-cuda:
     # Clone vllm
 	pip install -U ninja packaging --no-cache-dir
-	git clone --single-branch --branch $(BRANCH) $(REPOSITORY) vllm
+	git clone https://github.com/vllm-project/vllm.git vllm
 
-build-vllm: vllm
-	cd vllm && git fetch && git checkout $(VLLM_COMMIT)
+build-vllm-cuda: vllm-cuda
+	cd vllm && git fetch && git checkout f8a1e39fae05ca610be8d5a78be9d40f5274e5fc
 	cd vllm && python setup.py build
 
-install-vllm: build-vllm
+install-vllm-cuda: build-vllm-cuda
+	pip uninstall vllm -y || true
+	cd vllm && python setup.py install
+
+vllm-rocm:
+    # Clone vllm
+	pip install -U ninja packaging --no-cache-dir
+	git clone https://github.com/fxmarty/vllm-public.git vllm
+
+build-vllm-rocm: vllm-rocm
+	cd vllm && git fetch && git checkout ad9b7c4095ef54419a0533d254f2ad84bd2dfcae
+	cd vllm && python setup.py build
+
+install-vllm-rocm: build-vllm-rocm
 	pip uninstall vllm -y || true
 	cd vllm && python setup.py install
diff --git a/server/tests/models/test_bloom.py b/server/tests/models/test_bloom.py
index 1f70d000..303e9e71 100644
--- a/server/tests/models/test_bloom.py
+++ b/server/tests/models/test_bloom.py
@@ -135,8 +135,8 @@ def test_causal_lm_generate_token(default_bloom, default_bloom_batch):
     )
     assert all([generation.generated_text is None for generation in generations])
     assert all([len(generation.prefill_tokens) == 1 for generation in generations])
-    assert all([generation.token_id.item() == 10264 for generation in generations])
-    assert all([generation.token_text == "Test" for generation in generations])
+    assert all([token_id.item() == 10264 for generation in generations for token_id in generation.tokens.token_ids])
+    assert all([token_text == "Test" for generation in generations for token_text in generation.tokens.texts])
     assert generations[0].request_id == 0
 
 
diff --git a/server/tests/models/test_causal_lm.py b/server/tests/models/test_causal_lm.py
index e467d291..e9c2cd3a 100644
--- a/server/tests/models/test_causal_lm.py
+++ b/server/tests/models/test_causal_lm.py
@@ -141,8 +141,8 @@ def test_causal_lm_generate_token(default_causal_lm, default_causal_lm_batch):
     )
     assert all([generation.generated_text is None for generation in generations])
     assert all([len(generation.prefill_tokens) == 1 for generation in generations])
-    assert all([generation.token_id.item() == 13 for generation in generations])
-    assert all([generation.token_text == "." for generation in generations])
+    assert all([token_id.item() == 13 for generation in generations for token_id in generation.tokens.token_ids])
+    assert all([token_text == "." for generation in generations for token_text in generation.tokens.texts])
     assert generations[0].request_id == 0
 
 
diff --git a/server/tests/models/test_seq2seq_lm.py b/server/tests/models/test_seq2seq_lm.py
index 2b59f731..60be77c8 100644
--- a/server/tests/models/test_seq2seq_lm.py
+++ b/server/tests/models/test_seq2seq_lm.py
@@ -155,8 +155,8 @@ def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch)
     )
     assert all([generation.generated_text is None for generation in generations])
     assert all([len(generation.prefill_tokens) == 1 for generation in generations])
-    assert all([generation.token_id.item() == 259 for generation in generations])
-    assert all([generation.token_text == " " for generation in generations])
+    assert all([token_id.item() == 259 for generation in generations for token_id in generation.tokens.token_ids])
+    assert all([token_text == " " for generation in generations for token_text in generation.tokens.texts])
     assert generations[0].request_id == 0
 
 
diff --git a/server/text_generation_server/cli.py b/server/text_generation_server/cli.py
index fead2297..0817aee6 100644
--- a/server/text_generation_server/cli.py
+++ b/server/text_generation_server/cli.py
@@ -10,6 +10,7 @@ from pathlib import Path
 from loguru import logger
 from typing import Optional
 from enum import Enum
+from huggingface_hub import hf_hub_download
 
 
 app = typer.Typer()
@@ -31,6 +32,7 @@ def serve(
     revision: Optional[str] = None,
     sharded: bool = False,
     quantize: Optional[Quantization] = None,
+    speculate: Optional[int] = None,
     dtype: Optional[Dtype] = None,
     trust_remote_code: bool = False,
     uds_path: Path = "/tmp/text-generation-server",
@@ -39,9 +41,15 @@ def serve(
     otlp_endpoint: Optional[str] = None,
 ):
     if sharded:
-        assert os.getenv("WORLD_SIZE", None) is not None, "WORLD_SIZE must be set when sharded is True"
-        assert os.getenv("MASTER_ADDR", None) is not None, "MASTER_ADDR must be set when sharded is True"
-        assert os.getenv("MASTER_PORT", None) is not None, "MASTER_PORT must be set when sharded is True"
+        assert (
+            os.getenv("WORLD_SIZE", None) is not None
+        ), "WORLD_SIZE must be set when sharded is True"
+        assert (
+            os.getenv("MASTER_ADDR", None) is not None
+        ), "MASTER_ADDR must be set when sharded is True"
+        assert (
+            os.getenv("MASTER_PORT", None) is not None
+        ), "MASTER_PORT must be set when sharded is True"
 
     # Remove default handler
     logger.remove()
@@ -75,7 +83,11 @@ def serve(
         logger.info("CLI SHARDED = {}".format(num_shard))
         import subprocess
 
-        cmd = f"deepspeed --num_nodes 1 --num_gpus {num_shard} --no_local_rank {tgi_file} --model_id {model_id} --revision {revision} --sharded {sharded} --dtype {dtype} --uds_path {uds_path}"
+        cmd = f"deepspeed --num_nodes 1 --num_gpus {num_shard} --no_local_rank {tgi_file}"
+        cmd += f" --model_id {model_id} --revision {revision} --sharded {sharded}"
+        cmd += f" --dtype {dtype} --trust_remote_code {trust_remote_code} --uds_path {uds_path}"
+        if speculate is not None:
+            cmd += f"--speculate {speculate}"
         logger.info("CLI server start deepspeed ={} ".format(cmd))
         sys.stdout.flush()
         sys.stderr.flush()
@@ -119,7 +131,9 @@ def serve(
                 logger.error(f"{cmd}  exited with status = {proc.returncode}")
                 return proc.returncode
     else:
-        server.serve(model_id, revision, dtype, uds_path, sharded)
+        server.serve(
+            model_id, revision, sharded, speculate, dtype, trust_remote_code, uds_path
+        )
 
 
 @app.command()
@@ -153,7 +167,7 @@ def download_weights(
         logger.info("Files are already present on the host. " "Skipping download.")
         return
     # Local files not found
-    except (utils.LocalEntryNotFoundError, FileNotFoundError):
+    except (utils.LocalEntryNotFoundError, FileNotFoundError, utils.EntryNotFoundError):
         pass
 
     is_local_model = (Path(model_id).exists() and Path(model_id).is_dir()) or os.getenv(
@@ -161,6 +175,42 @@ def download_weights(
     ) is not None
 
     if not is_local_model:
+        try:
+            adapter_config_filename = hf_hub_download(
+                model_id, revision=revision, filename="adapter_config.json"
+            )
+            utils.download_and_unload_peft(
+                model_id, revision, trust_remote_code=trust_remote_code
+            )
+            is_local_model = True
+            utils.weight_files(model_id, revision, extension)
+            return
+        except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
+            pass
+
+        try:
+            import json
+            medusa_head = hf_hub_download(model_id, revision=revision, filename="medusa_lm_head.pt")
+            if auto_convert:
+                medusa_sf = Path(medusa_head[:-len(".pt")] + ".safetensors")
+                if not medusa_sf.exists():
+                    utils.convert_files([Path(medusa_head)], [medusa_sf], [])
+            medusa_config = hf_hub_download(model_id, revision=revision, filename="config.json")
+            with open(medusa_config, "r") as f:
+                config = json.load(f)
+
+            model_id = config["base_model_name_or_path"]
+            revision = "main"
+            try:
+                utils.weight_files(model_id, revision, extension)
+                logger.info(f"Files for parent {model_id} are already present on the host. " "Skipping download.")
+                return
+            # Local files not found
+            except (utils.LocalEntryNotFoundError, FileNotFoundError, utils.EntryNotFoundError):
+                pass
+        except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
+            pass
+
         # Try to download weights from the hub
         try:
             filenames = utils.weight_hub_files(model_id, revision, extension)
diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py
index efe9b62a..f7377c6b 100644
--- a/server/text_generation_server/models/__init__.py
+++ b/server/text_generation_server/models/__init__.py
@@ -1,10 +1,14 @@
 import torch
 
 from loguru import logger
+from transformers.configuration_utils import PretrainedConfig
 from transformers.models.auto import modeling_auto
-from transformers import AutoConfig
 from typing import Optional
 
+# Needed to properly setup habana_frameworks
+import text_generation_server.habana_quantization_env as hq_env
+
+from text_generation_server.utils.speculate import get_speculate, set_speculate
 from text_generation_server.models.model import Model
 from text_generation_server.models.causal_lm import CausalLM
 from text_generation_server.models.bloom import BLOOM
@@ -18,10 +22,46 @@ torch.set_grad_enabled(False)
 def get_model(
     model_id: str,
     revision: Optional[str],
-    dtype: Optional[torch.dtype] = None,
+    speculate: Optional[int],
+    dtype: Optional[torch.dtype],
+    trust_remote_code: bool,
 ) -> Model:
-    config = AutoConfig.from_pretrained(model_id, revision=revision)
-    model_type = config.model_type
+    if speculate is not None:
+        set_speculate(speculate)
+    else:
+        set_speculate(0)
+
+    config_dict, _ = PretrainedConfig.get_config_dict(
+        model_id, revision=revision, trust_remote_code=trust_remote_code
+    )
+
+    use_medusa = None
+    if "medusa_num_heads" in config_dict:
+        use_medusa = model_id
+        medusa_config = config_dict
+        model_id = config_dict["base_model_name_or_path"]
+        revision = "main"
+        speculate_medusa = config_dict["medusa_num_heads"]
+        if speculate is not None:
+            if speculate > speculate_medusa:
+                raise RuntimeError("Speculate is set to `{speculate}` but this medusa models only has `{speculate_medusa}` heads, please make them match")
+            else:
+                set_speculate(speculate)
+        else:
+            set_speculate(speculate_medusa)
+
+        config_dict, _ = PretrainedConfig.get_config_dict(
+            model_id, revision=revision, trust_remote_code=trust_remote_code
+        )
+        method = "medusa"
+    else:
+        method = "n-gram"
+
+    speculate = get_speculate()
+    if speculate > 0:
+        logger.info(f"Using speculation {method} with {speculate} input ids.")
+
+    model_type = config_dict["model_type"]
 
     if model_type == "gpt_bigcode":
         return SantaCoder(model_id, revision, dtype)
diff --git a/server/text_generation_server/models/causal_lm.py b/server/text_generation_server/models/causal_lm.py
index bdc0b4c5..e8e63586 100644
--- a/server/text_generation_server/models/causal_lm.py
+++ b/server/text_generation_server/models/causal_lm.py
@@ -35,10 +35,9 @@ from text_generation_server.utils.tokens import batch_top_tokens
 from text_generation_server.models import Model
 from text_generation_server.models.types import (
     Batch,
-    PrefillTokens,
+    Tokens,
     Generation,
     GeneratedText,
-    TopTokens,
 )
 from text_generation_server.pb import generate_pb2
 from text_generation_server.utils import (
@@ -48,6 +47,7 @@ from text_generation_server.utils import (
     is_tokenizer_transparent,
 )
 from text_generation_server.utils.debug import dbg_trace
+from text_generation_server.utils.speculate import get_speculate
 
 tracer = trace.get_tracer(__name__)
 
@@ -647,6 +647,8 @@ class CausalLM(Model):
             kwargs["attn_softmax_bf16"] = True
             kwargs["trim_logits"] = True
 
+        self.speculate = get_speculate()
+
         super(CausalLM, self).__init__(
             model=model,
             tokenizer=tokenizer,
@@ -842,12 +844,12 @@ class CausalLM(Model):
                 # Select next token
                 input_length = batch.input_length
                 if logits.shape[-2] > 1:
-                    next_token_ids, next_token_logprobs, logprobs = batch.next_token_chooser(
-                        batch.input_ids, logits[:, input_length - 1: input_length, :].squeeze(-2)
+                    next_token_ids, next_token_logprobs, logprobs, _, _ = batch.next_token_chooser(
+                        batch.input_ids, logits[:, input_length - 1: input_length, :].squeeze(-2), self.speculate
                     )
                 else:
-                    next_token_ids, next_token_logprobs, logprobs = batch.next_token_chooser(
-                        batch.input_ids, logits.squeeze(-2)
+                    next_token_ids, next_token_logprobs, logprobs, _, _ = batch.next_token_chooser(
+                        batch.input_ids, logits.squeeze(-2), self.speculate
                     )
                 batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
                     batch.top_n_tokens,
@@ -1017,7 +1019,9 @@ class CausalLM(Model):
                         clean_up_tokenization_spaces=False,
                         skip_special_tokens=False,
                     )
-                    prefill_tokens = PrefillTokens(prefill_token_ids, prefill_logprobs, prefill_texts)
+                    prefill_tokens = Tokens(
+                        prefill_token_ids, prefill_logprobs, prefill_texts, is_special=[]
+                    )
                 else:
                     prefill_tokens = None
 
@@ -1027,8 +1031,10 @@ class CausalLM(Model):
                         clean_up_tokenization_spaces=False,
                         skip_special_tokens=False,
                     )
-                    special_toptokens = [token_id in self.all_special_ids for token_id in top_token_ids]
-                    top_tokens = TopTokens(
+                    special_toptokens = [
+                        token_id in self.all_special_ids for token_id in top_token_ids
+                    ]
+                    top_tokens = Tokens(
                         top_token_ids,
                         top_token_logprobs,
                         toptoken_texts,
@@ -1040,10 +1046,12 @@ class CausalLM(Model):
                 generation = Generation(
                     request.id,
                     prefill_tokens,
-                    next_token_id,
-                    next_token_logprob,
-                    next_token_text,
-                    next_token_id in self.all_special_ids,
+                    Tokens(
+                        [next_token_id],
+                        [next_token_logprob],
+                        [next_token_text],
+                        [next_token_id in self.all_special_ids],
+                    ),
                     generated_text,
                     top_tokens,
                 )
diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py
index f1a4854f..79344ea1 100644
--- a/server/text_generation_server/models/flash_causal_lm.py
+++ b/server/text_generation_server/models/flash_causal_lm.py
@@ -11,13 +11,13 @@ from opentelemetry import trace
 from transformers import PreTrainedTokenizerBase
 from typing import Optional, Tuple, List, Type, Union, Dict
 
-from text_generation_server.models import Model
+from text_generation_server.models import Model 
+from text_generation_server.utils.speculate import get_speculate
 from text_generation_server.models.types import (
     Batch,
-    PrefillTokens,
+    Tokens,
     Generation,
     GeneratedText,
-    TopTokens,
 )
 from text_generation_server.models.cache_manager import (
     get_cache_manager,
@@ -41,6 +41,7 @@ class FlashCausalLMBatch(Batch):
     # Decoder values
     input_ids: torch.Tensor
     position_ids: torch.Tensor
+    speculative_ids: torch.Tensor
 
     # Flash Attention values
 
@@ -120,6 +121,7 @@ class FlashCausalLMBatch(Batch):
         )["input_ids"]
 
         position_ids = []
+        speculative_ids = []
         cu_seqlen_prefill = [0]
         needed_blocks_slots = []
         start_slots = []
@@ -163,6 +165,8 @@ class FlashCausalLMBatch(Batch):
             input_length = len(tokenized_input)
             input_lengths.append(input_length)
 
+
+
             prefix_offsets.append(input_length - 5)
             read_offsets.append(input_length)
 
@@ -186,7 +190,8 @@ class FlashCausalLMBatch(Batch):
 
             # Paged attention
             # Remove one as the first token des not have a past
-            total_tokens = input_length + max_new_tokens - 1
+            speculative_length = get_speculate()
+            total_tokens = input_length + max_new_tokens - 1 + speculative_length
             needed_blocks = math.ceil(total_tokens / BLOCK_SIZE)
             blocks += needed_blocks
             needed_blocks_slots.append((needed_blocks, total_tokens))
@@ -224,7 +229,7 @@ class FlashCausalLMBatch(Batch):
             cumulative_max_length += total_tokens
             max_seqlen = max(max_seqlen, input_length)
             max_blocks = max(max_blocks, needed_blocks)
-            max_length = max(max_length, input_length + max_new_tokens)
+            max_length = max(max_length, input_length + max_new_tokens + speculative_length)
 
         next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
             next_token_chooser_parameters, dtype, device
@@ -255,7 +260,6 @@ class FlashCausalLMBatch(Batch):
         cu_seqlen_prefill = torch.tensor(
             cu_seqlen_prefill, device=device, dtype=torch.int32
         )
-
         position_ids = position_ids.to(device)
         slot_indices = slot_indices.to(device)
         input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
@@ -309,6 +313,7 @@ class FlashCausalLMBatch(Batch):
             top_n_tokens_tensor=top_n_tokens_tensor,
             blocks=blocks,
             max_blocks=max_blocks,
+            speculative_ids=None,
         )
 
     @tracer.start_as_current_span("filter")
@@ -419,6 +424,7 @@ class FlashCausalLMBatch(Batch):
         slots = self.slots[slot_filtering_indices]
         next_token_chooser = self.next_token_chooser.filter(indices)
         top_n_tokens_tensor = self.top_n_tokens_tensor[indices]
+        speculative_ids = self.speculative_ids[indices] if self.speculative_ids is not None else None
 
         start_slots = torch.tensor(start_slots, dtype=torch.int64)
 
@@ -454,6 +460,7 @@ class FlashCausalLMBatch(Batch):
             top_n_tokens_tensor=top_n_tokens_tensor,
             blocks=blocks,
             max_blocks=max_blocks,
+            speculative_ids=speculative_ids,
         )
 
     @classmethod
@@ -473,6 +480,7 @@ class FlashCausalLMBatch(Batch):
             total_batch_size += len(b)
             total_slots += len(b.slots)
             blocks += b.blocks
+            speculative_length = b.speculative_ids.shape[1] if b.speculative_ids is not None else 0
             max_blocks = max(max_blocks, b.max_blocks)
             max_seqlen = max(max_seqlen, b.max_seqlen)
             max_length = max(
@@ -480,6 +488,7 @@ class FlashCausalLMBatch(Batch):
                 max(
                     input_length
                     + stopping_criteria.max_new_tokens
+                    + speculative_length
                     - stopping_criteria.current_tokens
                     for input_length, stopping_criteria in zip(
                         b.input_lengths, b.stopping_criterias
@@ -577,6 +586,8 @@ class FlashCausalLMBatch(Batch):
             device=batches[0].next_token_chooser.device,
         )
 
+        speculative_ids = torch.cat([b.speculative_ids for b in batches], dim=0) if batches[0].speculative_ids is not None else None
+
         # Needed to avoid dropping blocks when the batches will go out of scope
         for b in batches:
             b.block_tables = None
@@ -611,6 +622,7 @@ class FlashCausalLMBatch(Batch):
             top_n_tokens_tensor=top_n_tokens_tensor,
             blocks=blocks,
             max_blocks=max_blocks,
+            speculative_ids=speculative_ids
         )
 
     def __del__(self):
@@ -714,16 +726,55 @@ class FlashCausalLM(Model):
 
     def forward(self, batch: FlashCausalLMBatch) -> Tuple[torch.Tensor, 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=get_cache_manager().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)
+
+            # 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=get_cache_manager().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
+
         return self.model.forward(
-            input_ids=batch.input_ids,
-            position_ids=batch.position_ids,
-            cu_seqlen_prefill=batch.cu_seqlen_prefill,
-            kv_cache=get_cache_manager().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,
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlen_prefill=cu_seqlen_prefill,
+            kv_cache=kv_cache,
+            block_tables=block_tables,
+            slots=slots,
+            input_lengths=input_lengths,
+            max_s=max_s,
+            lm_head_indices=lm_head_indices,
         )
 
     @tracer.start_as_current_span("generate_token")
@@ -752,21 +803,32 @@ class FlashCausalLM(Model):
             del batch
             raise e
 
+        if isinstance(out, tuple):
+            out, speculative_logits = out
+        else:
+            speculative_logits = None
+
+
         if prefill:
             next_token_logits = (
                 out[batch.prefill_next_token_indices] if prefill_logprobs else out
             )
+            if speculative_logits is not None:
+                speculative_logits = (
+                    speculative_logits[batch.prefill_next_token_indices] if prefill_logprobs else speculative_logits
+                )
         else:
             next_token_logits = out
 
-        next_input_ids, next_token_logprobs, logprobs = batch.next_token_chooser(
-            batch.all_input_ids_tensor[:, : batch.max_seqlen], next_token_logits
+        next_input_ids, next_token_logprobs, logprobs, accepted_ids, speculative_ids = batch.next_token_chooser(
+            batch.all_input_ids_tensor[:, : batch.max_seqlen], next_token_logits, get_speculate(), batch.speculative_ids, speculative_logits
         )
 
         batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
             batch.top_n_tokens, batch.top_n_tokens_tensor, logprobs
         )
 
+        speculative_length = 0 if speculative_ids is None else speculative_ids.shape[1]
         if prefill:
             if len(batch) > 1 and prefill_logprobs:
                 # We create the prefill_tokens_indices tensor that will be used to gather prefill logprobs
@@ -792,6 +854,7 @@ class FlashCausalLM(Model):
         iterator = zip(
             batch.input_lengths,
             batch.all_input_ids,
+            accepted_ids
         )
 
         # We do two for loops as the first one can run completely asynchronously from the GPU while for the second
@@ -799,9 +862,11 @@ class FlashCausalLM(Model):
         # It is faster if we delay this sync for the maximum amount of time
 
         # For each member of the batch
+        index = 0
         for i, (
             input_length,
             all_input_ids,
+            n_accepted_ids
         ) in enumerate(iterator):
             # Indexing metadata
             start_index = cumulative_length
@@ -830,15 +895,18 @@ class FlashCausalLM(Model):
                             start_index + 1 : start_index + out_length
                         ]
 
-            batch.all_input_ids_tensor[i, input_length] = next_input_ids[i]
+            for j in range(n_accepted_ids):
+                batch.all_input_ids_tensor[i, input_length + j] = next_input_ids[index]
+                index += 1
 
             cumulative_length += input_length
 
-        # Set values in batch
-        batch.input_ids = next_input_ids
-        batch.position_ids = next_position_ids + 1
-        batch.input_lengths_tensor += 1
-        batch.slot_indices += 1
+
+        batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1]
+        batch.speculative_ids = speculative_ids
+        batch.position_ids = next_position_ids + accepted_ids
+        batch.input_lengths_tensor += accepted_ids
+        batch.slot_indices += accepted_ids
 
         if prefill and prefill_logprobs:
             # Get prefill logprobs
@@ -851,7 +919,7 @@ class FlashCausalLM(Model):
 
         # GPU <-> CPU sync
         next_token_logprobs = next_token_logprobs.tolist()
-        next_token_ids = batch.input_ids.tolist()
+        next_token_ids = next_input_ids.tolist()
 
         # Zipped iterator
         iterator = zip(
@@ -864,13 +932,13 @@ class FlashCausalLM(Model):
             batch.next_token_chooser.do_sample,
             batch.next_token_chooser.seeds,
             batch.top_n_tokens,
-            next_token_ids,
-            next_token_logprobs,
+            accepted_ids,
             batch_top_token_ids,
             batch_top_token_logprobs,
         )
 
         # For each member of the batch
+        index = 0
         for i, (
             request,
             input_length,
@@ -881,29 +949,43 @@ class FlashCausalLM(Model):
             do_sample,
             seed,
             top_n_tokens,
-            next_token_id,
-            next_token_logprob,
+            n_accepted_ids,
             top_token_ids,
             top_token_logprobs,
         ) in enumerate(iterator):
             # Append next token to all tokens
-            all_input_ids.append(next_token_id)
+            next_token_texts = []
+            left = 0
+            before = stopping_criteria.current_tokens
 
-            # Generated token
-            next_token_text, prefix_offset, read_offset = self.decode_token(
-                all_input_ids,
-                prefix_offset,
-                read_offset,
-            )
+            current_stopped = False
+            for j in range(index, index + n_accepted_ids):
+                # Generated token
+                next_token_id = next_token_ids[j]
+                all_input_ids.append(next_token_id)
+                next_token_text, prefix_offset, read_offset = self.decode_token(
+                    all_input_ids,
+                    prefix_offset,
+                    read_offset,
+                )
+                next_token_texts.append(next_token_text)
 
-            # Evaluate stopping criteria
-            stop, reason = stopping_criteria(
-                next_token_id,
-                next_token_text,
-            )
+                stop, reason = stopping_criteria(
+                    next_token_id,
+                    next_token_text,
+                )
 
-            if not stop:
-                stopped = False
+                if stop:
+                    left = index + n_accepted_ids - j - 1
+                    current_stopped = True
+                    break
+                else:
+                    current_stopped = False
+            stopped = stopped and current_stopped
+
+            _next_token_ids = next_token_ids[index: index+n_accepted_ids - left]
+            _next_token_logprobs = next_token_logprobs[index: index+n_accepted_ids - left]
+            index += n_accepted_ids
 
             # Shard generations
             # All generations will be appended in the rust sharded client
@@ -943,8 +1025,9 @@ class FlashCausalLM(Model):
                         clean_up_tokenization_spaces=False,
                         skip_special_tokens=False,
                     )
-                    prefill_tokens = PrefillTokens(
-                        prefill_token_ids, request_prefill_logprobs, prefill_texts
+
+                    prefill_tokens = Tokens(
+                        prefill_token_ids, request_prefill_logprobs, prefill_texts, is_special = []
                     )
                 else:
                     prefill_tokens = None
@@ -958,7 +1041,7 @@ class FlashCausalLM(Model):
                     special_toptokens = [
                         token_id in self.all_special_ids for token_id in top_token_ids
                     ]
-                    top_tokens = TopTokens(
+                    top_tokens = Tokens(
                         top_token_ids,
                         top_token_logprobs,
                         toptoken_texts,
@@ -970,10 +1053,12 @@ class FlashCausalLM(Model):
                 generation = Generation(
                     request.id,
                     prefill_tokens,
-                    next_token_id,
-                    next_token_logprob,
-                    next_token_text,
-                    next_token_id in self.all_special_ids,
+                    Tokens(
+                        _next_token_ids,
+                        _next_token_logprobs,
+                        next_token_texts,
+                        [nid in self.all_special_ids for nid in _next_token_ids],
+                    ),
                     generated_text,
                     top_tokens,
                 )
@@ -981,7 +1066,9 @@ class FlashCausalLM(Model):
                 generations.append(generation)
 
             # Update values
-            batch.input_lengths[i] = input_length + 1
+            batch.input_lengths[i] = input_length + n_accepted_ids.item()
+            if batch.input_lengths[i] > batch.max_seqlen:
+                batch.max_seqlen = batch.input_lengths[i]
             batch.prefix_offsets[i] = prefix_offset
             batch.read_offsets[i] = read_offset
             batch.all_input_ids[i] = all_input_ids
@@ -994,6 +1081,5 @@ class FlashCausalLM(Model):
         batch.prefill_cu_outlens = None
         batch.prefill_head_indices = None
         batch.prefill_next_token_indices = None
-        batch.max_seqlen = batch.max_seqlen + 1
 
         return generations, batch
diff --git a/server/text_generation_server/models/flash_llama.py b/server/text_generation_server/models/flash_llama.py
index d2ed0b15..3a84b1b6 100644
--- a/server/text_generation_server/models/flash_llama.py
+++ b/server/text_generation_server/models/flash_llama.py
@@ -28,6 +28,7 @@ class FlashLlama(FlashCausalLM):
         quantize: Optional[str] = None,
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
+        use_medusa: Optional[str] = None,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
@@ -66,6 +67,18 @@ class FlashLlama(FlashCausalLM):
             weights._set_gptq_params(model_id)
 
         model = FlashLlamaForCausalLM(config, weights)
+        if use_medusa:
+            from text_generation_server.utils.medusa import MedusaModel
+            from huggingface_hub import hf_hub_download
+            import json
+            medusa_config = hf_hub_download(use_medusa, revision=revision, filename="config.json")
+            with open(medusa_config, "r") as f:
+                config = json.load(f)
+            medusa_head = hf_hub_download(use_medusa, revision=revision, filename="medusa_lm_head.pt")
+            medusa_sf = medusa_head[:-len(".pt")] + ".safetensors"
+            weights = Weights([medusa_sf], device, dtype, process_group=self.process_group)
+            lm_head = model.lm_head
+            model.lm_head = MedusaModel(config, weights, lm_head)
 
         torch.distributed.barrier(group=self.process_group)
         super(FlashLlama, self).__init__(
diff --git a/server/text_generation_server/models/flash_mistral.py b/server/text_generation_server/models/flash_mistral.py
index 919e4625..e103d9fc 100644
--- a/server/text_generation_server/models/flash_mistral.py
+++ b/server/text_generation_server/models/flash_mistral.py
@@ -21,6 +21,7 @@ from text_generation_server.models.custom_modeling.flash_mistral_modeling import
     FlashMistralForCausalLM,
     MistralConfig,
 )
+from text_generation_server.utils.speculate import get_speculate
 from text_generation_server.utils import (
     initialize_torch_distributed,
     weight_files,
@@ -132,7 +133,8 @@ class FlashMistralBatch(FlashCausalLMBatch):
 
             # Paged attention
             # Remove one as the first token des not have a past
-            total_tokens = input_length + max_new_tokens - 1
+            speculative_length = get_speculate()
+            total_tokens = input_length + max_new_tokens - 1 + speculative_length
 
             # Needed blocks can not go over SLIDING_WINDOW_BLOCKS
             needed_blocks = min(
@@ -183,7 +185,7 @@ class FlashMistralBatch(FlashCausalLMBatch):
             cumulative_max_length += total_tokens
             max_seqlen = max(max_seqlen, input_length)
             max_blocks = max(max_blocks, needed_blocks)
-            max_length = max(max_length, input_length + max_new_tokens)
+            max_length = max(max_length, input_length + max_new_tokens + speculative_length)
 
         next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
             next_token_chooser_parameters, dtype, device
@@ -272,6 +274,7 @@ class FlashMistralBatch(FlashCausalLMBatch):
             blocks=blocks,
             max_blocks=max_blocks,
             prefill_cache_indices=prefill_cache_indices,
+            speculative_ids=None
         )
 
 
@@ -340,17 +343,55 @@ class FlashMistral(FlashCausalLM):
 
     def forward(self, batch: FlashMistralBatch) -> Tuple[torch.Tensor, 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=get_cache_manager().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)
+
+            # 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=get_cache_manager().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
         logits = self.model.forward(
-            input_ids=batch.input_ids,
-            position_ids=batch.position_ids,
-            cu_seqlen_prefill=batch.cu_seqlen_prefill,
-            kv_cache=get_cache_manager().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,
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlen_prefill=cu_seqlen_prefill,
+            kv_cache=kv_cache,
+            block_tables=block_tables,
+            slots=slots,
+            input_lengths=input_lengths,
+            max_s=max_s,
             prefill_cache_indices=batch.prefill_cache_indices,
-            lm_head_indices=batch.prefill_head_indices,
+            lm_head_indices=lm_head_indices,
         )
         if batch.prefill_cache_indices is not None:
             batch.prefill_cache_indices = None
diff --git a/server/text_generation_server/models/idefics_causal_lm.py b/server/text_generation_server/models/idefics_causal_lm.py
index dcad1fa9..2f4bb139 100644
--- a/server/text_generation_server/models/idefics_causal_lm.py
+++ b/server/text_generation_server/models/idefics_causal_lm.py
@@ -20,7 +20,7 @@ from typing import Optional, Tuple, List, Type, Dict
 from text_generation_server.models import Model
 from text_generation_server.models.types import (
     Batch,
-    PrefillTokens,
+    Tokens,
     Generation,
     GeneratedText,
 )
@@ -791,8 +791,8 @@ class IdeficsCausalLM(Model):
                         clean_up_tokenization_spaces=False,
                         skip_special_tokens=False,
                     )
-                    prefill_tokens = PrefillTokens(
-                        prefill_token_ids, prefill_logprobs, prefill_texts
+                    prefill_tokens = Tokens(
+                        prefill_token_ids, prefill_logprobs, prefill_texts, is_special=[]
                     )
                 else:
                     prefill_tokens = None
@@ -802,10 +802,12 @@ class IdeficsCausalLM(Model):
                 generation = Generation(
                     request.id,
                     prefill_tokens,
-                    next_token_id_squeezed,
-                    next_token_logprob,
-                    next_token_text,
-                    next_token_id_squeezed.item() in self.all_special_ids,
+                    Tokens(
+                    [next_token_id_squeezed],
+                    [next_token_logprob],
+                    [next_token_text],
+                    [next_token_id_squeezed.item() in self.all_special_ids],
+                    ),
                     generated_text,
                     top_tokens,
                 )
diff --git a/server/text_generation_server/models/model.py b/server/text_generation_server/models/model.py
index 73e1f1af..52be52ff 100644
--- a/server/text_generation_server/models/model.py
+++ b/server/text_generation_server/models/model.py
@@ -5,7 +5,8 @@ from abc import ABC, abstractmethod
 from typing import List, Optional, Tuple, Type, TypeVar
 from transformers import PreTrainedTokenizerBase
 
-from text_generation_server.models.types import Batch, GeneratedText
+from text_generation_server.models.types import Batch, Generation
+from text_generation_server.utils.speculate import get_speculate
 from text_generation_server.pb.generate_pb2 import InfoResponse
 
 B = TypeVar("B", bound=Batch)
@@ -22,6 +23,7 @@ class Model(ABC):
         rank: int = 0,
         world_size: int = 1,
         kwargs: dict = {},
+        speculate: Optional[int] = None,
     ):
         self.model = model
         self.tokenizer = tokenizer
@@ -32,7 +34,14 @@ class Model(ABC):
         self.rank = rank
         self.world_size = world_size
         self.kwargs = kwargs
-        self.has_position_ids = inspect.signature(model.forward).parameters.get("position_ids", None) is not None
+        if speculate is None:
+            speculate = get_speculate()
+        self.speculate = speculate
+
+        self.has_position_ids = (
+            inspect.signature(model.forward).parameters.get("position_ids", None)
+            is not None
+        )
 
         self.check_initialized()
 
@@ -42,6 +51,7 @@ class Model(ABC):
             requires_padding=self.requires_padding,
             dtype=str(self.dtype),
             device_type=self.device.type,
+            speculate=self.speculate
         )
 
     @property
@@ -50,7 +60,7 @@ class Model(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def generate_token(self, batch: B) -> Tuple[List[GeneratedText], Optional[B]]:
+    def generate_token(self, batch: B) -> Tuple[List[Generation], Optional[B]]:
         raise NotImplementedError
 
     def warmup(self, batch: B, max_total_tokens: int):
diff --git a/server/text_generation_server/models/seq2seq_lm.py b/server/text_generation_server/models/seq2seq_lm.py
index d4d3cd19..279b5505 100644
--- a/server/text_generation_server/models/seq2seq_lm.py
+++ b/server/text_generation_server/models/seq2seq_lm.py
@@ -11,8 +11,7 @@ from text_generation_server.models.types import (
     GeneratedText,
     Batch,
     Generation,
-    PrefillTokens,
-    TopTokens,
+    Tokens,
 )
 from text_generation_server.pb import generate_pb2
 from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
@@ -733,10 +732,11 @@ class Seq2SeqLM(Model):
 
                 # Prefill
                 if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
-                    prefill_tokens = PrefillTokens(
+                    prefill_tokens = Tokens(
                         [self.tokenizer.bos_token_id],
                         [float("nan")],
                         [self.tokenizer.bos_token],
+                        [False]
                     )
                 else:
                     prefill_tokens = None
@@ -750,7 +750,7 @@ class Seq2SeqLM(Model):
                     special_toptokens = [
                         token_id in self.all_special_ids for token_id in top_token_ids
                     ]
-                    top_tokens = TopTokens(
+                    top_tokens = Tokens(
                         top_token_ids,
                         top_token_logprobs,
                         toptoken_texts,
@@ -762,10 +762,12 @@ class Seq2SeqLM(Model):
                 generation = Generation(
                     request.id,
                     prefill_tokens,
-                    next_token_id_squeezed,
-                    next_token_logprob,
-                    next_token_text,
-                    next_token_id_squeezed.item() in self.all_special_ids,
+                    Tokens(
+                    [next_token_id_squeezed],
+                    [next_token_logprob],
+                    [next_token_text],
+                    [next_token_id_squeezed.item() in self.all_special_ids],
+                    ),
                     generated_text,
                     top_tokens,
                 )
diff --git a/server/text_generation_server/models/types.py b/server/text_generation_server/models/types.py
index 0e27680d..87c03d63 100644
--- a/server/text_generation_server/models/types.py
+++ b/server/text_generation_server/models/types.py
@@ -58,33 +58,15 @@ class GeneratedText:
 
 
 @dataclass
-class PrefillTokens:
-    token_ids: List[int]
-    logprobs: List[float]
-    texts: List[str]
-
-    def to_pb(self) -> generate_pb2.PrefillTokens:
-        return generate_pb2.PrefillTokens(
-            ids=self.token_ids, logprobs=self.logprobs, texts=self.texts
-        )
-
-    def __len__(self):
-        return len(self.token_ids)
-
-
-@dataclass
-class TopTokens:
+class Tokens:
     token_ids: List[int]
     logprobs: List[float]
     texts: List[str]
     is_special: List[bool]
 
-    def to_pb(self) -> generate_pb2.TopTokens:
-        return generate_pb2.TopTokens(
-            ids=self.token_ids,
-            logprobs=self.logprobs,
-            texts=self.texts,
-            is_special=self.is_special,
+    def to_pb(self) -> generate_pb2.Tokens:
+        return generate_pb2.Tokens(
+            ids=self.token_ids, logprobs=self.logprobs, texts=self.texts, is_special=self.is_special
         )
 
     def __len__(self):
@@ -94,14 +76,11 @@ class TopTokens:
 @dataclass
 class Generation:
     request_id: int
-    prefill_tokens: Optional[PrefillTokens]
-    token_id: int
-    token_logprob: float
-    token_text: str
-    token_is_special: bool
+    prefill_tokens: Optional[Tokens]
+    tokens: Tokens
     generated_text: Optional[GeneratedText]
     # Optional for now, since it's not yet supported for every model.
-    top_tokens: Optional[TopTokens]
+    top_tokens: Optional[List[Tokens]]
 
     def to_pb(self) -> generate_pb2.Generation:
         return generate_pb2.Generation(
@@ -109,10 +88,7 @@ class Generation:
             prefill_tokens=self.prefill_tokens.to_pb()
             if self.prefill_tokens is not None
             else None,
-            token_id=self.token_id,
-            token_logprob=self.token_logprob,
-            token_text=self.token_text,
-            token_is_special=self.token_is_special,
+            tokens=self.tokens.to_pb(),
             generated_text=self.generated_text.to_pb()
             if self.generated_text is not None
             else None,
diff --git a/server/text_generation_server/server.py b/server/text_generation_server/server.py
index 67358d3c..33a26bd2 100644
--- a/server/text_generation_server/server.py
+++ b/server/text_generation_server/server.py
@@ -107,9 +107,11 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
 def serve(
     model_id: str,
     revision: Optional[str],
-    dtype: Optional[str],
-    uds_path: Path,
     sharded: bool,
+    speculate: Optional[int],
+    dtype: Optional[str],
+    trust_remote_code: bool,
+    uds_path: Path,
 ):
     # Remove default handler
     logger.remove()
@@ -126,8 +128,10 @@ def serve(
     async def serve_inner(
         model_id: str,
         revision: Optional[str],
-        dtype: Optional[str] = None,
         sharded: bool = False,
+        speculate: Optional[int] = None,
+        dtype: Optional[str] = None,
+        trust_remote_code: bool = False,
     ):
         unix_socket_template = "unix://{}-{}"
         logger.info("Server:server_inner: sharded ={}".format(sharded))
@@ -151,7 +155,9 @@ def serve(
         if revision == "None":
             revision = None
         try:
-            model = get_model(model_id, revision=revision, dtype=data_type)
+            model = get_model(
+                model_id, revision, speculate, dtype=data_type, trust_remote_code=trust_remote_code
+            )
         except Exception:
             logger.exception("Error when initializing model")
             raise
@@ -181,13 +187,7 @@ def serve(
         except KeyboardInterrupt:
             logger.info("Signal received. Shutting down")
             await server.stop(0)
-        finally:
-            if hasattr(model,'finish_quantization_measurements'):
-                model.finish_quantization_measurements()
 
-    logger.info(
-        "Starting Server : model_id= {}, revision = {}  dtype = {}  sharded = {} ".format(
-            model_id, revision, dtype, sharded
-        )
+    asyncio.run(
+        serve_inner(model_id, revision, sharded, speculate, dtype, trust_remote_code)
     )
-    asyncio.run(serve_inner(model_id, revision, dtype, sharded))
diff --git a/server/text_generation_server/tgi_service.py b/server/text_generation_server/tgi_service.py
index bf1bab40..f88c8c8b 100644
--- a/server/text_generation_server/tgi_service.py
+++ b/server/text_generation_server/tgi_service.py
@@ -9,12 +9,18 @@ import argparse
 def main(args):
     logger.info("TGIService: starting tgi service .... ")
     logger.info(
-        "TGIService: --model_id {}, --revision {}, --sharded {}, --dtype {}, --uds_path {} ".format(
-            args.model_id, args.revision, args.sharded, args.dtype, args.uds_path
+        "TGIService: --model_id {}, --revision {}, --sharded {}, --speculate {}, --dtype {}, --trust_remote_code {}, --uds_path {} ".format(
+            args.model_id, args.revision, args.sharded, args.speculate, args.dtype, args.trust_remote_code, args.uds_path
         )
     )
     server.serve(
-        model_id=args.model_id, revision=args.revision, dtype=args.dtype, uds_path=args.uds_path, sharded=args.sharded
+        model_id=args.model_id,
+        revision=args.revision,
+        sharded=args.sharded,
+        speculate=args.speculate,
+        dtype=args.dtype,
+        trust_remote_code=args.trust_remote_code,
+        uds_path=args.uds_path,
     )
 
 
@@ -23,7 +29,9 @@ if __name__ == "__main__":
     parser.add_argument("--model_id", type=str)
     parser.add_argument("--revision", type=str)
     parser.add_argument("--sharded", type=bool)
+    parser.add_argument("--speculate", type=int, default=None)
     parser.add_argument("--dtype", type=str)
+    parser.add_argument("--trust_remote_code", type=bool)
     parser.add_argument("--uds_path", type=Path)
     args = parser.parse_args()
     main(args)
diff --git a/server/text_generation_server/utils/medusa.py b/server/text_generation_server/utils/medusa.py
new file mode 100644
index 00000000..029de122
--- /dev/null
+++ b/server/text_generation_server/utils/medusa.py
@@ -0,0 +1,51 @@
+import torch
+from dataclasses import dataclass
+from text_generation_server.utils.layers import TensorParallelHead, FastLinear
+
+@dataclass
+class Output:
+    logits: torch.FloatTensor = None
+    speculative_logits: torch.FloatTensor = None
+
+
+class ResBlock(torch.nn.Module):
+    def __init__(self, config, prefix, weights):
+        super().__init__()
+        self.linear = FastLinear.load(config, prefix=f"{prefix}.linear", weights=weights, bias=True)
+        self.act = torch.nn.SiLU()
+
+    def forward(self, x):
+        return x + self.act(self.linear(x))
+
+
+class MedusaModel(torch.nn.Module):
+    def __init__(
+        self,
+        config,
+        weights,
+        lm_head
+    ):
+        super().__init__()
+        self.heads = torch.nn.ModuleList(
+            [MedusaHead(config, prefix=f"{i}", weights=weights) for i in range(config["medusa_num_heads"])]
+        )
+        self.lm_head = lm_head
+
+    def forward(self, x):
+        logits = self.lm_head(x)
+        speculative_logits = torch.stack([head(x) for head in self.heads], dim=1)
+        return logits, speculative_logits
+
+
+class MedusaHead(torch.nn.Module):
+    def __init__(self, config, prefix, weights):
+        super().__init__()
+        self.blocks = torch.nn.ModuleList([ResBlock(config, prefix=f"{prefix}.{i}", weights=weights) for i in range(config["medusa_num_layers"])])
+        n = len(self.blocks)
+        self.out = FastLinear.load(config, prefix=f"{prefix}.{n}", weights=weights, bias=False)
+
+    def forward(self, x):
+        for block in self.blocks:
+            x = block(x)
+        x = self.out(x)
+        return x
diff --git a/server/text_generation_server/utils/speculate.py b/server/text_generation_server/utils/speculate.py
new file mode 100644
index 00000000..38a91972
--- /dev/null
+++ b/server/text_generation_server/utils/speculate.py
@@ -0,0 +1,12 @@
+
+SPECULATE = None
+
+def get_speculate() -> int:
+    global SPECULATE
+    return SPECULATE
+
+def set_speculate(speculate: int):
+    global SPECULATE
+    SPECULATE = speculate
+
+
diff --git a/server/text_generation_server/utils/tokens.py b/server/text_generation_server/utils/tokens.py
index c50d10e3..acd55f26 100644
--- a/server/text_generation_server/utils/tokens.py
+++ b/server/text_generation_server/utils/tokens.py
@@ -142,6 +142,22 @@ class StoppingCriteria:
         )
 
 
+def create_n_gram_speculation(input_ids: torch.Tensor, next_ids: torch.Tensor, accepted_ids: torch.Tensor, speculate: int, verbose: bool):
+    # Very trivial approach, find first match in the string.
+    # This is much less refined than actual n-gram but seems to work
+    # relatively OK in grounded mode and is by far much faster with
+    # much less worst case complexity as everything happens on device.
+    B = accepted_ids.shape[0]
+    device = input_ids.device
+    seeds = next_ids[accepted_ids.cumsum(dim=-1) -1 ]
+    indices = (input_ids == seeds.unsqueeze(-1)).max(dim=1).indices + 1
+    all_indices = indices.unsqueeze(-1).expand(B, speculate) + torch.arange(speculate, device=device)
+    all_indices = torch.clamp(all_indices, max=input_ids.shape[1] - 1)
+
+    speculative_ids = input_ids.gather(dim=-1, index=all_indices)
+    return speculative_ids
+
+
 class HeterogeneousNextTokenChooser:
     def __init__(
         self,
@@ -206,16 +222,72 @@ class HeterogeneousNextTokenChooser:
         self.dtype = dtype
         self.device = device
 
-    def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor):
-        if self.watermark_processor is not None:
-            scores = self.watermark_processor(input_ids, scores)
-        if self.repetition_processor is not None:
-            scores = self.repetition_processor(input_ids, scores)
+    def __call__(
+        self,
+        input_ids: torch.Tensor,
+        scores: torch.Tensor,
+        speculate: int,
+        speculated_ids: Optional[torch.Tensor] = None,
+        speculative_scores: Optional[torch.Tensor] = None,
+        verbose=False
+    ):
+        if speculated_ids is not None:
+            B = scores.shape[0] // (speculated_ids.shape[1] + 1)
+            S = speculated_ids.shape[1] + 1
+            scores = scores.view(B, S, -1)
+        else:
+            B = scores.shape[0]
+            S = 1
+            scores = scores.view(B, S, -1)
 
-        for warper in self.warpers:
-            scores = warper(input_ids, scores)
+        next_ids = torch.zeros((B, S), device=scores.device, dtype=torch.long)
+        for j in range(S):
+            _scores = scores[:, j]
+            if self.watermark_processor is not None:
+                _scores = self.watermark_processor(input_ids, _scores)
+            if self.repetition_processor is not None:
+                _scores = self.repetition_processor(input_ids, _scores)
+
+            for warper in self.warpers:
+                _scores = warper(input_ids, _scores)
+
+            _next_ids = self.choice(_scores)
+            scores[:, j] = _scores
+            next_ids[:, j] = _next_ids
+        next_ids = next_ids.view(B*S)
+        scores = scores.view( B* S, -1)
+
+        if speculated_ids is not None:
+            accepted_ids = []
+            B = next_ids.shape[0] // (speculated_ids.shape[1] + 1)
+            S = speculated_ids.shape[1] + 1
+            indices = []
+            for i in range(B):
+                _next_ids = next_ids[i*S: (i + 1)*S]
+                _speculated_ids = speculated_ids[i]
+                validate_speculative = _next_ids[:-1] == _speculated_ids
+                index = i * S
+                accepted = 1
+                # First is always valid
+                indices.append(index)
+                for valid in validate_speculative.tolist():
+                    if valid:
+                        index += 1
+                        accepted += 1
+                        indices.append(index)
+                    else:
+                        break
+                accepted_ids.append(accepted)
+
+            accepted_ids = torch.tensor(accepted_ids, device=input_ids.device, dtype=input_ids.dtype)
+            next_ids = next_ids[indices]
+            scores = scores[indices]
+            indices = torch.arange(B, device=input_ids.device) * S
+            if speculative_scores is not None:
+                speculative_scores = speculative_scores[indices + accepted_ids - 1]
+        else:
+            accepted_ids = torch.ones_like(next_ids)
 
-        next_ids = self.choice(scores)
         # ignore logprobs if we use greedy search
         if type(self.choice) == Greedy:
             logprobs = torch.empty_like(scores, device="cpu")
@@ -224,7 +296,17 @@ class HeterogeneousNextTokenChooser:
             logprobs = torch.log_softmax(scores, -1)
             next_logprobs = torch.gather(logprobs, 1, next_ids.view(-1, 1)).view(-1)
 
-        return next_ids, next_logprobs, logprobs
+        if speculate > 0:
+            if speculative_scores is not None:
+                # Medusa provided some scores
+                speculative_ids = Greedy()(speculative_scores)
+            else:
+                # n-gram
+                speculative_ids = create_n_gram_speculation(input_ids, next_ids, accepted_ids, speculate, verbose)
+        else:
+            speculative_ids = None
+
+        return next_ids, next_logprobs, logprobs, accepted_ids, speculative_ids
 
     def filter(self, indices):
         if self.watermark_processor is not None: