diff --git a/server/text_generation_server/utils/gptq/custom_autotune.py b/server/text_generation_server/utils/gptq/custom_autotune.py
new file mode 100644
index 00000000..875c832e
--- /dev/null
+++ b/server/text_generation_server/utils/gptq/custom_autotune.py
@@ -0,0 +1,193 @@
+#https://github.com/fpgaminer/GPTQ-triton
+"""
+Mostly the same as the autotuner in Triton, but with a few changes like using 40 runs instead of 100.
+"""
+
+import builtins
+import math
+import time
+from typing import Dict
+
+import triton
+
+
+class Autotuner(triton.KernelInterface):
+
+    def __init__(self, fn, arg_names, configs, key, reset_to_zero, prune_configs_by: Dict = None, nearest_power_of_two: bool = False):
+        '''
+		:param prune_configs_by: a dict of functions that are used to prune configs, fields:
+			'perf_model': performance model used to predicate running time with different configs, returns running time
+			'top_k': number of configs to bench
+			'prune_num_stages_by'(optional): a function used to prune num_stages. It take configs:List[Config] as its input, and returns pruned configs.
+			'nearest_power_of_two'(optional): whether to round key arguments to the nearest power of two when caching tuning results
+		'''
+        if not configs:
+            self.configs = [triton.Config({}, num_warps=4, num_stages=2)]
+        else:
+            self.configs = configs
+        self.key_idx = [arg_names.index(k) for k in key]
+        self.nearest_power_of_two = nearest_power_of_two
+        self.cache = {}
+        # hook to reset all required tensor to zeros before relaunching a kernel
+        self.hook = lambda args: 0
+        if reset_to_zero is not None:
+            self.reset_idx = [arg_names.index(k) for k in reset_to_zero]
+
+            def _hook(args):
+                for i in self.reset_idx:
+                    args[i].zero_()
+
+            self.hook = _hook
+        self.arg_names = arg_names
+        # prune configs
+        if prune_configs_by:
+            perf_model, top_k = prune_configs_by['perf_model'], prune_configs_by['top_k']
+            if 'early_config_prune' in prune_configs_by:
+                early_config_prune = prune_configs_by['early_config_prune']
+        else:
+            perf_model, top_k, early_config_prune = None, None, None
+        self.perf_model, self.configs_top_k = perf_model, top_k
+        self.early_config_prune = early_config_prune
+        self.fn = fn
+
+    def _bench(self, *args, config, **meta):
+        # check for conflicts, i.e. meta-parameters both provided
+        # as kwargs and by the autotuner
+        conflicts = meta.keys() & config.kwargs.keys()
+        if conflicts:
+            raise ValueError(f"Conflicting meta-parameters: {', '.join(conflicts)}."
+                             " Make sure that you don't re-define auto-tuned symbols.")
+        # augment meta-parameters with tunable ones
+        current = dict(meta, **config.kwargs)
+
+        def kernel_call():
+            if config.pre_hook:
+                config.pre_hook(self.nargs)
+            self.hook(args)
+            self.fn.run(*args, num_warps=config.num_warps, num_stages=config.num_stages, **current)
+
+        try:
+            # In testings using only 40 reps seems to be close enough and it appears to be what PyTorch uses
+            # PyTorch also sets fast_flush to True, but I didn't see any speedup so I'll leave the default
+            return triton.testing.do_bench(kernel_call, percentiles=(0.5, 0.2, 0.8), rep=40)
+        except triton.compiler.OutOfResources:
+            return (float('inf'), float('inf'), float('inf'))
+
+    def run(self, *args, **kwargs):
+        self.nargs = dict(zip(self.arg_names, args))
+        if len(self.configs) > 1:
+            key = tuple(args[i] for i in self.key_idx)
+
+            # This reduces the amount of autotuning by rounding the keys to the nearest power of two
+            # In my testing this gives decent results, and greatly reduces the amount of tuning required
+            if self.nearest_power_of_two:
+                key = tuple([2**int(math.log2(x) + 0.5) for x in key])
+
+            if key not in self.cache:
+                # prune configs
+                pruned_configs = self.prune_configs(kwargs)
+                bench_start = time.time()
+                timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
+                bench_end = time.time()
+                self.bench_time = bench_end - bench_start
+                self.cache[key] = builtins.min(timings, key=timings.get)
+                self.hook(args)
+                self.configs_timings = timings
+            config = self.cache[key]
+        else:
+            config = self.configs[0]
+        self.best_config = config
+        if config.pre_hook is not None:
+            config.pre_hook(self.nargs)
+        return self.fn.run(*args, num_warps=config.num_warps, num_stages=config.num_stages, **kwargs, **config.kwargs)
+
+    def prune_configs(self, kwargs):
+        pruned_configs = self.configs
+        if self.early_config_prune:
+            pruned_configs = self.early_config_prune(self.configs, self.nargs)
+        if self.perf_model:
+            top_k = self.configs_top_k
+            if isinstance(top_k, float) and top_k <= 1.0:
+                top_k = int(len(self.configs) * top_k)
+            if len(pruned_configs) > top_k:
+                est_timing = {config: self.perf_model(**self.nargs, **kwargs, **config.kwargs, num_stages=config.num_stages, num_warps=config.num_warps) for config in pruned_configs}
+                pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[:top_k]
+        return pruned_configs
+
+    def warmup(self, *args, **kwargs):
+        self.nargs = dict(zip(self.arg_names, args))
+        for config in self.prune_configs(kwargs):
+            self.fn.warmup(
+                *args,
+                num_warps=config.num_warps,
+                num_stages=config.num_stages,
+                **kwargs,
+                **config.kwargs,
+            )
+        self.nargs = None
+
+
+def autotune(configs, key, prune_configs_by=None, reset_to_zero=None, nearest_power_of_two=False):
+    """
+	Decorator for auto-tuning a :code:`triton.jit`'d function.
+	.. highlight:: python
+	.. code-block:: python
+		@triton.autotune(configs=[
+			triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4),
+			triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8),
+			],
+			key=['x_size'] # the two above configs will be evaluated anytime
+							# the value of x_size changes
+		)
+		@triton.jit
+		def kernel(x_ptr, x_size, **META):
+			BLOCK_SIZE = META['BLOCK_SIZE']
+	:note: When all the configurations are evaluated, the kernel will run multiple time.
+			This means that whatever value the kernel updates will be updated multiple times.
+			To avoid this undesired behavior, you can use the `reset_to_zero` argument, which
+			reset the value of the provided tensor to `zero` before running any configuration.
+	:param configs: a list of :code:`triton.Config` objects
+	:type configs: list[triton.Config]
+	:param key: a list of argument names whose change in value will trigger the evaluation of all provided configs.
+	:type key: list[str]
+	:param prune_configs_by: a dict of functions that are used to prune configs, fields:
+		'perf_model': performance model used to predicate running time with different configs, returns running time
+		'top_k': number of configs to bench
+		'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It take configs:List[Config] as its input, and returns pruned configs.
+	:param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs.
+	:type reset_to_zero: list[str]
+	"""
+
+    def decorator(fn):
+        return Autotuner(fn, fn.arg_names, configs, key, reset_to_zero, prune_configs_by, nearest_power_of_two)
+
+    return decorator
+
+
+def matmul248_kernel_config_pruner(configs, nargs):
+    """
+    The main purpose of this function is to shrink BLOCK_SIZE_* when the corresponding dimension is smaller.
+    """
+    m = max(2**int(math.ceil(math.log2(nargs['M']))), 16)
+    n = max(2**int(math.ceil(math.log2(nargs['N']))), 16)
+    k = max(2**int(math.ceil(math.log2(nargs['K']))), 16)
+
+    used = set()
+    for config in configs:
+        block_size_m = min(m, config.kwargs['BLOCK_SIZE_M'])
+        block_size_n = min(n, config.kwargs['BLOCK_SIZE_N'])
+        block_size_k = min(k, config.kwargs['BLOCK_SIZE_K'])
+        group_size_m = config.kwargs['GROUP_SIZE_M']
+
+        if (block_size_m, block_size_n, block_size_k, group_size_m, config.num_stages, config.num_warps) in used:
+            continue
+
+        used.add((block_size_m, block_size_n, block_size_k, group_size_m, config.num_stages, config.num_warps))
+        yield triton.Config({
+            'BLOCK_SIZE_M': block_size_m,
+            'BLOCK_SIZE_N': block_size_n,
+            'BLOCK_SIZE_K': block_size_k,
+            'GROUP_SIZE_M': group_size_m
+        },
+                            num_stages=config.num_stages,
+                            num_warps=config.num_warps)
diff --git a/server/text_generation_server/utils/gptq/quant_linear.py b/server/text_generation_server/utils/gptq/quant_linear.py
new file mode 100644
index 00000000..fee1bb29
--- /dev/null
+++ b/server/text_generation_server/utils/gptq/quant_linear.py
@@ -0,0 +1,444 @@
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.cuda.amp import custom_bwd, custom_fwd
+
+try:
+    import triton
+    import triton.language as tl
+    from . import custom_autotune
+
+    # code based https://github.com/fpgaminer/GPTQ-triton
+    @custom_autotune.autotune(
+        configs=[
+            triton.Config({
+                'BLOCK_SIZE_M': 64,
+                'BLOCK_SIZE_N': 256,
+                'BLOCK_SIZE_K': 32,
+                'GROUP_SIZE_M': 8
+            }, num_stages=4, num_warps=4),
+            triton.Config({
+                'BLOCK_SIZE_M': 128,
+                'BLOCK_SIZE_N': 128,
+                'BLOCK_SIZE_K': 32,
+                'GROUP_SIZE_M': 8
+            }, num_stages=4, num_warps=4),
+            triton.Config({
+                'BLOCK_SIZE_M': 64,
+                'BLOCK_SIZE_N': 128,
+                'BLOCK_SIZE_K': 32,
+                'GROUP_SIZE_M': 8
+            }, num_stages=4, num_warps=4),
+            triton.Config({
+                'BLOCK_SIZE_M': 128,
+                'BLOCK_SIZE_N': 32,
+                'BLOCK_SIZE_K': 32,
+                'GROUP_SIZE_M': 8
+            }, num_stages=4, num_warps=4),
+            triton.Config({
+                'BLOCK_SIZE_M': 64,
+                'BLOCK_SIZE_N': 64,
+                'BLOCK_SIZE_K': 32,
+                'GROUP_SIZE_M': 8
+            }, num_stages=4, num_warps=4),
+            triton.Config({
+                'BLOCK_SIZE_M': 64,
+                'BLOCK_SIZE_N': 128,
+                'BLOCK_SIZE_K': 32,
+                'GROUP_SIZE_M': 8
+            }, num_stages=2, num_warps=8),
+            triton.Config({
+                'BLOCK_SIZE_M': 64,
+                'BLOCK_SIZE_N': 64,
+                'BLOCK_SIZE_K': 64,
+                'GROUP_SIZE_M': 8
+            }, num_stages=3, num_warps=8),
+            triton.Config({
+                'BLOCK_SIZE_M': 32,
+                'BLOCK_SIZE_N': 32,
+                'BLOCK_SIZE_K': 128,
+                'GROUP_SIZE_M': 8
+            }, num_stages=2, num_warps=4),
+        ],
+        key=['M', 'N', 'K'],
+        nearest_power_of_two=True,
+        prune_configs_by={
+            'early_config_prune': custom_autotune.matmul248_kernel_config_pruner,
+            'perf_model': None,
+            'top_k': None,
+        },
+    )
+    @triton.jit
+    def matmul_248_kernel(a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, g_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales, stride_zeros,
+                          BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr):
+        """
+        Compute the matrix multiplication C = A x B.
+        A is of shape (M, K) float16
+        B is of shape (K//8, N) int32
+        C is of shape (M, N) float16
+        scales is of shape (G, N) float16
+        zeros is of shape (G, N) float16
+        g_ptr is of shape (K) int32 
+        """
+        infearure_per_bits = 32 // bits
+
+        pid = tl.program_id(axis=0)
+        num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
+        num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+        num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
+        num_pid_in_group = GROUP_SIZE_M * num_pid_n
+        group_id = pid // num_pid_in_group
+        first_pid_m = group_id * GROUP_SIZE_M
+        group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+        pid_m = first_pid_m + (pid % group_size_m)
+        pid_n = (pid % num_pid_in_group) // group_size_m
+
+        offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+        offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+        offs_k = tl.arange(0, BLOCK_SIZE_K)
+        a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
+        a_mask = (offs_am[:, None] < M)
+        # b_ptrs is set up such that it repeats elements along the K axis 8 times
+        b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn)  # (BLOCK_SIZE_K, BLOCK_SIZE_N)
+        g_ptrs = g_ptr + offs_k
+        # shifter is used to extract the N bits of each element in the 32-bit word from B
+        scales_ptrs = scales_ptr + offs_bn[None, :]
+        zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
+
+        shifter = (offs_k % infearure_per_bits) * bits
+        zeros_shifter = (offs_bn % infearure_per_bits) * bits
+        accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+
+        for k in range(0, num_pid_k):
+            g_idx = tl.load(g_ptrs)
+
+            # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
+            scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+            zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+
+            zeros = (zeros >> zeros_shifter[None, :]) & maxq
+            zeros = (zeros + 1)
+
+            a = tl.load(a_ptrs, mask=a_mask, other=0.)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
+            b = tl.load(b_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
+
+            # Now we need to unpack b (which is N-bit values) into 32-bit values
+            b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
+            b = (b - zeros) * scales  # Scale and shift
+
+            accumulator += tl.dot(a, b)
+            a_ptrs += BLOCK_SIZE_K
+            b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
+            g_ptrs += BLOCK_SIZE_K
+
+        c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
+        c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
+        tl.store(c_ptrs, accumulator, mask=c_mask)
+
+    @custom_autotune.autotune(configs=[
+        triton.Config({
+            'BLOCK_SIZE_M': 64,
+            'BLOCK_SIZE_N': 32,
+            'BLOCK_SIZE_K': 256,
+            'GROUP_SIZE_M': 8
+        }, num_stages=4, num_warps=4),
+        triton.Config({
+            'BLOCK_SIZE_M': 128,
+            'BLOCK_SIZE_N': 32,
+            'BLOCK_SIZE_K': 128,
+            'GROUP_SIZE_M': 8
+        }, num_stages=4, num_warps=4),
+        triton.Config({
+            'BLOCK_SIZE_M': 64,
+            'BLOCK_SIZE_N': 32,
+            'BLOCK_SIZE_K': 128,
+            'GROUP_SIZE_M': 8
+        }, num_stages=4, num_warps=4),
+        triton.Config({
+            'BLOCK_SIZE_M': 128,
+            'BLOCK_SIZE_N': 32,
+            'BLOCK_SIZE_K': 32,
+            'GROUP_SIZE_M': 8
+        }, num_stages=4, num_warps=4),
+        triton.Config({
+            'BLOCK_SIZE_M': 64,
+            'BLOCK_SIZE_N': 32,
+            'BLOCK_SIZE_K': 64,
+            'GROUP_SIZE_M': 8
+        }, num_stages=4, num_warps=4),
+        triton.Config({
+            'BLOCK_SIZE_M': 64,
+            'BLOCK_SIZE_N': 32,
+            'BLOCK_SIZE_K': 128,
+            'GROUP_SIZE_M': 8
+        }, num_stages=2, num_warps=8),
+        triton.Config({
+            'BLOCK_SIZE_M': 64,
+            'BLOCK_SIZE_N': 64,
+            'BLOCK_SIZE_K': 64,
+            'GROUP_SIZE_M': 8
+        }, num_stages=3, num_warps=8),
+        triton.Config({
+            'BLOCK_SIZE_M': 32,
+            'BLOCK_SIZE_N': 128,
+            'BLOCK_SIZE_K': 32,
+            'GROUP_SIZE_M': 8
+        }, num_stages=2, num_warps=4),
+    ],
+                              key=['M', 'N', 'K'],
+                              nearest_power_of_two=True)
+    @triton.jit
+    def transpose_matmul_248_kernel(a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, g_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales,
+                                    stride_zeros, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr):
+        """
+        Compute the matrix multiplication C = A x B.
+        A is of shape (M, N) float16
+        B is of shape (K//8, N) int32
+        C is of shape (M, K) float16
+        scales is of shape (G, N) float16
+        zeros is of shape (G, N) float16
+        g_ptr is of shape (K) int32 
+        """
+        infearure_per_bits = 32 // bits
+
+        pid = tl.program_id(axis=0)
+        num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
+        num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
+        num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+        num_pid_in_group = GROUP_SIZE_M * num_pid_k
+        group_id = pid // num_pid_in_group
+        first_pid_m = group_id * GROUP_SIZE_M
+        group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+        pid_m = first_pid_m + (pid % group_size_m)
+        pid_k = (pid % num_pid_in_group) // group_size_m
+
+        offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+        offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
+        offs_n = tl.arange(0, BLOCK_SIZE_N)
+        a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak)  # (BLOCK_SIZE_M, BLOCK_SIZE_N)
+        a_mask = (offs_am[:, None] < M)
+        # b_ptrs is set up such that it repeats elements along the K axis 8 times
+        b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn)  # (BLOCK_SIZE_K, BLOCK_SIZE_N)
+        g_ptrs = g_ptr + offs_bk
+        g_idx = tl.load(g_ptrs)
+
+        # shifter is used to extract the N bits of each element in the 32-bit word from B
+        scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
+        zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros
+
+        shifter = (offs_bk % infearure_per_bits) * bits
+        zeros_shifter = (offs_n % infearure_per_bits) * bits
+        accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
+
+        for n in range(0, num_pid_n):
+            # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
+            scales = tl.load(scales_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+            zeros = tl.load(zeros_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+
+            zeros = (zeros >> zeros_shifter[None, :]) & maxq
+            zeros = (zeros + 1)
+
+            a = tl.load(a_ptrs, mask=a_mask, other=0.)  # (BLOCK_SIZE_M, BLOCK_SIZE_N)
+            b = tl.load(b_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
+
+            # Now we need to unpack b (which is N-bit values) into 32-bit values
+            b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
+            b = (b - zeros) * scales  # Scale and shift
+            b = tl.trans(b)
+
+            accumulator += tl.dot(a, b)
+            a_ptrs += BLOCK_SIZE_N
+            b_ptrs += BLOCK_SIZE_N
+            scales_ptrs += BLOCK_SIZE_N
+            zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
+
+        c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
+        c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
+        tl.store(c_ptrs, accumulator, mask=c_mask)
+except:
+    print('trioton not installed.')
+
+
+def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
+    with torch.cuda.device(input.device):
+        output = torch.empty((input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16)
+        grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']), )
+        matmul_248_kernel[grid](input, qweight, output, scales, qzeros, g_idx, input.shape[0], qweight.shape[1], input.shape[1], bits, maxq, input.stride(0), input.stride(1), qweight.stride(0),
+                                qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0))
+        return output
+
+
+def transpose_matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
+    with torch.cuda.device(input.device):
+        output_dim = (qweight.shape[0] * 32) // bits
+        output = torch.empty((input.shape[0], output_dim), device=input.device, dtype=torch.float16)
+        grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_dim, META['BLOCK_SIZE_K']), )
+        transpose_matmul_248_kernel[grid](input, qweight, output, scales, qzeros, g_idx, input.shape[0], qweight.shape[1], output_dim, bits, maxq, input.stride(0), input.stride(1), qweight.stride(0),
+                                          qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0))
+        return output
+
+
+class QuantLinearFunction(torch.autograd.Function):
+
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float16)
+    def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
+        output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
+        ctx.save_for_backward(qweight, scales, qzeros, g_idx)
+        ctx.bits, ctx.maxq = bits, maxq
+        return output
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, grad_output):
+        qweight, scales, qzeros, g_idx = ctx.saved_tensors
+        bits, maxq = ctx.bits, ctx.maxq
+        grad_input = None
+
+        if ctx.needs_input_grad[0]:
+            grad_input = transpose_matmul248(grad_output, qweight, scales, qzeros, g_idx, bits, maxq)
+        return grad_input, None, None, None, None, None, None
+
+
+class QuantLinear(nn.Module):
+    def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
+        super().__init__()
+        self.qweight = qweight
+        self.qzeros = qzeros
+        self.scales = scales
+        self.g_idx = g_idx
+        self.bias = bias
+        if bits not in [2, 4, 8]:
+            raise NotImplementedError("Only 2,4,8 bits are supported.")
+        self.bits = bits
+        self.maxq = 2**self.bits - 1
+        self.groupsize = groupsize
+
+        self.outfeatures = qweight.shape[1]
+        self.infeatures = qweight.shape[0] * 32 // 4
+
+        # expected = (math.ceil(self.infeatures / self.groupsize), self.outfeatures // 32 * self.bits)
+        # assert tuple(self.qzeros.shape) == expected, f"{self.qzeros.shape} != {expected}"
+        # expected = (math.ceil(self.infeatures / self.groupsize), self.outfeatures)
+        # assert tuple(self.scales.shape) == expected, f"{self.scales.shape} != {expected}"
+        # assert self.g_idx.shape == (math.ceil(self.infeatures / self.group_size), outfeatures)
+
+    # def new(cls, bits, groupsize, infeatures, outfeatures, bias):
+    #     if bits not in [2, 4, 8]:
+    #         raise NotImplementedError("Only 2,4,8 bits are supported.")
+
+    #     self.infeatures = infeatures
+    #     self.outfeatures = outfeatures
+    #     self.bits = bits
+    #     self.maxq = 2**self.bits - 1
+    #     self.groupsize = groupsize if groupsize != -1 else infeatures
+
+    #     self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
+    #     self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32))
+    #     self.register_buffer('scales', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16))
+    #     self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32))
+    #     if bias:
+    #         self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
+    #     else:
+    #         self.bias = None
+
+    def pack(self, linear, scales, zeros, g_idx=None):
+        self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
+
+        scales = scales.t().contiguous()
+        zeros = zeros.t().contiguous()
+        scale_zeros = zeros * scales
+        self.scales = scales.clone().half()
+        if linear.bias is not None:
+            self.bias = linear.bias.clone().half()
+
+        intweight = []
+        for idx in range(self.infeatures):
+            intweight.append(torch.round((linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[idx]]).to(torch.int)[:, None])
+        intweight = torch.cat(intweight, dim=1)
+        intweight = intweight.t().contiguous()
+        intweight = intweight.numpy().astype(np.uint32)
+        qweight = np.zeros((intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32)
+        i = 0
+        row = 0
+        while row < qweight.shape[0]:
+            if self.bits in [2, 4, 8]:
+                for j in range(i, i + (32 // self.bits)):
+                    qweight[row] |= intweight[j] << (self.bits * (j - i))
+                i += 32 // self.bits
+                row += 1
+            else:
+                raise NotImplementedError("Only 2,4,8 bits are supported.")
+
+        qweight = qweight.astype(np.int32)
+        self.qweight = torch.from_numpy(qweight)
+
+        zeros -= 1
+        zeros = zeros.numpy().astype(np.uint32)
+        qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32)
+        i = 0
+        col = 0
+        while col < qzeros.shape[1]:
+            if self.bits in [2, 4, 8]:
+                for j in range(i, i + (32 // self.bits)):
+                    qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
+                i += 32 // self.bits
+                col += 1
+            else:
+                raise NotImplementedError("Only 2,4,8 bits are supported.")
+
+        qzeros = qzeros.astype(np.int32)
+        self.qzeros = torch.from_numpy(qzeros)
+
+    def forward(self, x):
+        out_shape = x.shape[:-1] + (self.outfeatures, )
+        out = QuantLinearFunction.apply(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, self.g_idx, self.bits, self.maxq)
+        out = out + self.bias if self.bias is not None else out
+        return out.reshape(out_shape)
+
+
+def make_quant_linear(module, names, bits, groupsize, name=''):
+    if isinstance(module, QuantLinear):
+        return
+    for attr in dir(module):
+        tmp = getattr(module, attr)
+        name1 = name + '.' + attr if name != '' else attr
+        if name1 in names:
+            delattr(module, attr)
+            setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None))
+    for name1, child in module.named_children():
+        make_quant_linear(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)
+
+
+def autotune_warmup_linear(model, transpose=False):
+    """
+    Pre-tunes the quantized kernel
+    """
+    from tqdm import tqdm
+
+    kn_values = {}
+
+    for _, m in model.named_modules():
+        if not isinstance(m, QuantLinear):
+            continue
+
+        k = m.infeatures
+        n = m.outfeatures
+
+        if (k, n) not in kn_values:
+            kn_values[(k, n)] = (m.qweight.cuda(), m.scales.cuda(), m.qzeros.cuda(), m.g_idx.cuda(), m.bits, m.maxq)
+
+    print(f'Found {len(kn_values)} unique KN Linear values.')
+
+    print('Warming up autotune cache ...')
+    with torch.no_grad():
+        for m in tqdm(range(0, 12)):
+            m = 2**m  # [1, 2048]
+            for (k, n), (qweight, scales, qzeros, g_idx, bits, maxq) in kn_values.items():
+                a = torch.randn(m, k, dtype=torch.float16, device='cuda')
+                matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq)
+                if transpose:
+                    a = torch.randn(m, n, dtype=torch.float16, device='cuda')
+                    transpose_matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq)
+    del kn_values
diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py
index 93865d52..c9bbf898 100644
--- a/server/text_generation_server/utils/layers.py
+++ b/server/text_generation_server/utils/layers.py
@@ -15,6 +15,8 @@ except ImportError:
 
 from accelerate import init_empty_weights
 
+from text_generation_server.utils.gptq.quant_linear import QuantLinear
+
 
 # Monkey patching
 @classmethod
@@ -129,7 +131,20 @@ def get_linear(weight, bias, quantize):
         if bias is not None:
             linear.bias = nn.Parameter(bias)
     elif quantize == "gptq":
-        raise NotImplementedError("Soon")
+        try:
+            qweight, qzeros, scales, g_idx, bits, groupsize = weight
+        except Exception:
+            raise NotImplementedError(f"The passed weight is not `gptq` compatible, loader needs to be updated.")
+
+        linear = QuantLinear(
+            qweight,
+            qzeros,
+            scales,
+            g_idx, 
+            bias,
+            bits,
+            groupsize,
+        )
     else:
         raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
     return linear
@@ -152,8 +167,14 @@ class TensorParallelHead(SuperLayer):
     @staticmethod
     def load(config, prefix: str, weights):
         weight = weights.get_sharded(f"{prefix}.weight", dim=0)
+
+        # GPTQ doesn't quantize heads (nor embeddings)
+        if config.quantize == "gptq":
+            quantize = None
+        else:
+            quantize = config.quantize
         return TensorParallelHead(
-            get_linear(weight, bias=None, quantize=config.quantize),
+            get_linear(weight, bias=None, quantize=quantize),
             process_group=weights.process_group,
         )
 
@@ -205,15 +226,29 @@ class TensorParallelColumnLinear(SuperLayer):
 
     @classmethod
     def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int):
-        w = [weights.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
-        weight = torch.cat(w, dim=dim)
+        if config.quantize == "gptq":
+            qweight = torch.cat([weights.get_sharded(f"{p}.qweight", dim=1) for p in prefixes], dim=1)
+            qzeros = torch.cat([weights.get_sharded(f"{p}.qzeros", dim=1) for p in prefixes], dim=1)
+            scales = torch.cat([weights.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1)
+            w = [weights.get_tensor(f"{p}.g_idx") for p in prefixes]
+            for w2 in w[1:]:
+                torch.testing.assert_close(w2, w[0])
+            g_idx = w[0]
+            # TODO Get that from file to be more generic
+            bits = 4
+            groupsize = 128
+            weight = (qweight, qzeros, scales, g_idx, bits, groupsize)
+        else:
+            w = [weights.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
+            weight = torch.cat(w, dim=dim)
 
         if bias:
             b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes]
             bias = torch.cat(b, dim=0)
         else:
             bias = None
-        return cls(get_linear(weight, bias, config.quantize))
+        linear = get_linear(weight, bias, config.quantize)
+        return cls(linear)
 
 
 class TensorParallelRowLinear(SuperLayer):
@@ -223,7 +258,20 @@ class TensorParallelRowLinear(SuperLayer):
 
     @classmethod
     def load(cls, config, prefix: str, weights, bias: bool):
-        weight = weights.get_sharded(f"{prefix}.weight", dim=1)
+        if config.quantize == "gptq":
+            qweight = weights.get_sharded(f"{prefix}.qweight", dim=0)
+            qzeros = weights.get_tensor(f"{prefix}.qzeros")
+            scales = weights.get_tensor(f"{prefix}.scales")
+            g_idx = weights.get_sharded(f"{prefix}.g_idx", dim=0)
+
+            # TODO Get that from file to be more generic
+            bits = 4
+            groupsize = 128
+
+            weight = (qweight, qzeros, scales, g_idx, bits, groupsize)
+        else:
+            weight = weights.get_sharded(f"{prefix}.weight", dim=1)
+
         if bias and weights.process_group.rank() == 0:
             # Rank is only on the first rank process
             bias = weights.get_tensor(f"{prefix}.bias")
diff --git a/server/text_generation_server/utils/weights.py b/server/text_generation_server/utils/weights.py
index 76a4f65a..03e1c6cb 100644
--- a/server/text_generation_server/utils/weights.py
+++ b/server/text_generation_server/utils/weights.py
@@ -1,6 +1,7 @@
 from pathlib import Path
 from typing import List
 from safetensors import safe_open
+import torch
 
 
 class Weights:
@@ -46,7 +47,10 @@ class Weights:
         filename = self.get_filename(tensor_name)
         f = self._get_handle(filename)
         tensor = f.get_tensor(tensor_name)
-        tensor = tensor.to(dtype=self.dtype)
+        # Special case for gptq which shouldn't convert
+        # u4 which are disguised as int32
+        if tensor.dtype != torch.int32:
+            tensor = tensor.to(dtype=self.dtype)
         tensor = tensor.to(device=self.device)
         return tensor
 
@@ -72,6 +76,9 @@ class Weights:
             tensor = slice_[:, start:stop]
         else:
             raise NotImplementedError("Let's make that generic when needed")
-        tensor = tensor.to(dtype=self.dtype)
+        # Special case for gptq which shouldn't convert
+        # u4 which are disguised as int32
+        if tensor.dtype != torch.int32:
+            tensor = tensor.to(dtype=self.dtype)
         tensor = tensor.to(device=self.device)
         return tensor