diff --git a/.github/workflows/compile.yml b/.github/workflows/compile.yml
index 815dc3575..2b70beea6 100644
--- a/.github/workflows/compile.yml
+++ b/.github/workflows/compile.yml
@@ -95,6 +95,17 @@ jobs:
           python generate.py --checkpoint-path ${MODEL_PATH} --temperature 0 --dso-path ${MODEL_DIR}/${MODEL_NAME}.so  > ./output_aoti
           cat ./output_aoti
 
+          echo "******************************************"
+          echo "******** INT4 group-wise quantized *******"
+          echo "******************************************"
+          python generate.py --quant '{"linear:int4" : {"group_size": 32}}' --checkpoint-path ${MODEL_PATH} --temperature 0 > ./output_eager
+          cat ./output_eager
+          python generate.py --compile --quant '{"linear:int4" : {"group_size": 32}}' --checkpoint-path ${MODEL_PATH} --temperature 0 > ./output_compiled
+          cat ./output_compiled
+          python export.py --quant '{"linear:int4" : {"group_size": 32}}' --checkpoint-path ${MODEL_PATH} --output-dso-path ${MODEL_DIR}/${MODEL_NAME}.so
+          python generate.py --checkpoint-path ${MODEL_PATH} --temperature 0 --dso-path ${MODEL_DIR}/${MODEL_NAME}.so  > ./output_aoti
+          cat ./output_aoti
+
           echo "tests complete"
           echo "******************************************"
           # echo "********* EAGER vs TORCH.COMPILE *********"
diff --git a/quantize.py b/quantize.py
index 280e17810..0b89a1641 100644
--- a/quantize.py
+++ b/quantize.py
@@ -53,6 +53,12 @@ def quantize_model(model: nn.Module, quantize_options):
                 **q_kwargs
             ).quantized_model()
         elif quantizer == "linear:int4":
+            linears_quantized = True
+            model = WeightOnlyInt4QuantHandler(
+                model,
+                **q_kwargs
+            ).quantized_model()
+        elif quantizer == "linear:a8w4dq":
             linears_quantized = True
             model = Int8DynActInt4WeightQuantHandler(
                 model,
@@ -70,6 +76,9 @@ def quantize_model(model: nn.Module, quantize_options):
             assert 0 == 1, f"quantizer {quantizer} not supported"
             
     
+#########################################################################
+##### Quantization Primitives ######
+
 def dynamically_quantize_per_channel(
     x,
     quant_min,
@@ -164,6 +173,115 @@ def dynamically_quantize_per_channel(
     return quant, scales, zero_points
 
 
+
+def get_group_qparams(w, n_bit=4, groupsize=128, *, scales_dtype= torch.float):
+    # needed for GPTQ with padding
+    if groupsize > w.shape[-1]:
+        groupsize = w.shape[-1]
+    assert groupsize > 1
+    assert w.shape[-1] % groupsize == 0
+    assert w.dim() == 2
+
+    to_quant = w.reshape(-1, groupsize)
+    assert torch.isnan(to_quant).sum() == 0
+
+    max_val = to_quant.amax(dim=1, keepdim=True)
+    min_val = to_quant.amin(dim=1, keepdim=True)
+    max_int = 2**n_bit - 1
+    scales = (max_val - min_val).clamp(min=1e-6) / max_int
+    zeros = min_val + scales * (2 ** (n_bit - 1))
+    return scales.to(scales_dtype).reshape(w.shape[0], -1), zeros.to(
+        scales_dtype
+    ).reshape(w.shape[0], -1)
+
+
+def pack_scales_and_zeros(scales, zeros, *, scales_dtype=torch.float):
+    assert scales.shape == zeros.shape
+    assert scales.dtype == scales_dtype
+    assert zeros.dtype == scales_dtype
+    return (
+        torch.cat(
+            [
+                scales.reshape(scales.size(0), scales.size(1), 1),
+                zeros.reshape(zeros.size(0), zeros.size(1), 1),
+            ],
+            2,
+        )
+        .transpose(0, 1)
+        .contiguous()
+    )
+
+
+def unpack_scales_and_zeros(scales_and_zeros):
+    assert len(scales_and_zeros.shape) == 3 and scales_and_zeros.shape[2] == 2
+    assert scales_and_zeros.dtype == torch.float
+    return torch.split(scales_and_zeros.transpose(0, 1), 1, 2)
+
+
+def group_quantize_tensor_from_qparams(w, scales, zeros, n_bit=4, groupsize=128):
+    assert groupsize > 1
+    # needed for GPTQ single column quantize
+    if groupsize > w.shape[-1] and scales.shape[-1] == 1:
+        groupsize = w.shape[-1]
+
+    assert w.shape[-1] % groupsize == 0
+    assert w.dim() == 2
+
+    to_quant = w.reshape(-1, groupsize)
+    assert torch.isnan(to_quant).sum() == 0
+
+    scales = scales.reshape(-1, 1)
+    zeros = zeros.reshape(-1, 1)
+    min_val = zeros - scales * (2 ** (n_bit - 1))
+    max_int = 2**n_bit - 1
+    min_int = 0
+    w_int32 = (
+        to_quant.sub(min_val)
+        .div(scales)
+        .round()
+        .clamp_(min_int, max_int)
+        .to(torch.int32)
+        .reshape_as(w)
+    )
+
+    return w_int32
+
+
+def group_quantize_tensor(w, n_bit=4, groupsize=128):
+    scales, zeros = get_group_qparams(w, n_bit, groupsize)
+    w_int32 = group_quantize_tensor_from_qparams(w, scales, zeros, n_bit, groupsize)
+    scales_and_zeros = pack_scales_and_zeros(scales, zeros)
+    return w_int32, scales_and_zeros
+
+
+def group_dequantize_tensor_from_qparams(
+    w_int32, scales, zeros, n_bit=4, groupsize=128
+):
+    assert groupsize > 1
+    # needed for GPTQ single column dequantize
+    if groupsize > w_int32.shape[-1] and scales.shape[-1] == 1:
+        groupsize = w_int32.shape[-1]
+    assert w_int32.shape[-1] % groupsize == 0
+    assert w_int32.dim() == 2
+
+    w_int32_grouped = w_int32.reshape(-1, groupsize)
+    scales = scales.reshape(-1, 1)
+    zeros = zeros.reshape(-1, 1)
+
+    w_dq = (
+        w_int32_grouped.sub(2 ** (n_bit - 1)).mul(scales).add(zeros).reshape_as(w_int32)
+    )
+    return w_dq
+
+
+def group_dequantize_tensor(w_int32, scales_and_zeros, n_bit=4, groupsize=128):
+    scales, zeros = unpack_scales_and_zeros(scales_and_zeros)
+    return group_dequantize_tensor_from_qparams(
+        w_int32, scales, zeros, n_bit, groupsize
+    )
+
+#########################################################################
+
 class QuantHandler:
     def __init__(self, mod):
         self.mod = mod
@@ -173,6 +291,12 @@ def create_quantized_state_dict(self) -> Dict:  # "StateDict"
 
     def convert_for_runtime(self) -> nn.Module:
         pass
+    
+    def quantized_model(self) -> nn.Module:
+        model_updated_state_dict = self.create_quantized_state_dict()
+        self.convert_for_runtime()
+        self.mod.load_state_dict(model_updated_state_dict)
+        return self.mod
 
 
 ##### Weight-only int8 per-channel quantized code ######
@@ -202,7 +326,7 @@ def replace_linear_weight_only_int8_per_channel(module, node_type, group_size=No
             replace_linear_weight_only_int8_per_channel(child, node_type, group_size)
 
 
-class WeightOnlyInt8QuantHandler:
+class WeightOnlyInt8QuantHandler(QuantHandler):
     def __init__(
         self,
         mod,
@@ -349,7 +473,7 @@ def replace_embedding_weight_only_grouped_int8_per_channel(
             )
 
 
-class EmbeddingOnlyInt8QuantHandler:
+class EmbeddingOnlyInt8QuantHandler(QuantHandler):
     def __init__(self, mod, *, bitwidth: int = 8, group_size: Optional[int] = None):
         self.mod = mod
         self.group_size = group_size
@@ -466,6 +590,145 @@ def forward(self, indices: torch.Tensor) -> torch.Tensor:
 ##################################################################
 ##### weight only int4 per channel groupwise quantized code ######
 
+def _int4_prepare_int4_weight_and_scales_and_zeros(weight_bf16, groupsize, inner_k_tiles):
+    weight_int32, scales_and_zeros = group_quantize_tensor(
+        weight_bf16, n_bit=4, groupsize=groupsize
+    )
+    weight_int4pack = torch.ops.aten._convert_weight_to_int4pack(weight_int32, inner_k_tiles)
+    return weight_int4pack, scales_and_zeros
+
+def _int4_calc_padded_size(k, groupsize=1, innner_k_tiles=1):
+    from model import find_multiple
+    return find_multiple(k, 1024)
+
+def linear_forward_int4(x, weight_int4pack, scales_and_zeros, out_features, groupsize):
+    origin_x_size = x.size()
+    x = x.reshape(-1, origin_x_size[-1])
+    c = torch.ops.aten._weight_int4pack_mm(
+        x.to(dtype=torch.bfloat16),
+        weight_int4pack,
+        groupsize,
+        scales_and_zeros.to(dtype=torch.bfloat16)
+    ).to(dtype=x.dtype)
+    new_shape = origin_x_size[:-1] + (out_features,)
+    c = c.reshape(new_shape)
+    return c
+
+
+def _int4_check_linear_int4_k(k, groupsize = 1, inner_k_tiles = 1):
+    return k % groupsize == 0 and k % (inner_k_tiles * 16) == 0
+
+def replace_linear_int4(module, groupsize, inner_k_tiles, padding_allowed, use_cuda=False):
+    for name, child in module.named_children():
+        if isinstance(child, nn.Linear):
+            if _int4_check_linear_int4_k(child.in_features, groupsize, inner_k_tiles) or padding_allowed:
+                setattr(module, name, WeightOnlyInt4Linear(
+                    child.in_features, child.out_features, bias=False,
+                    groupsize=groupsize, inner_k_tiles=inner_k_tiles, use_cuda=use_cuda
+                ))
+        else:
+            replace_linear_int4(child, groupsize, inner_k_tiles, padding_allowed, use_cuda)
+
+
+class WeightOnlyInt4QuantHandler(QuantHandler):
+    def __init__(self, mod, group_size=128, inner_k_tiles=8, padding_allowed=True):
+        self.mod = mod
+        self.groupsize = group_size
+        self.inner_k_tiles = inner_k_tiles
+        self.padding_allowed = padding_allowed
+        assert group_size in [32, 64, 128, 256]
+        assert inner_k_tiles in [2, 4, 8]
+
+    @torch.no_grad()
+    def create_quantized_state_dict(self):
+        cur_state_dict = self.mod.state_dict()
+        for fqn, mod in self.mod.named_modules():
+            if isinstance(mod, torch.nn.Linear):
+                assert not mod.bias
+                out_features = mod.out_features
+                in_features = mod.in_features
+                assert out_features % 8 == 0, "require out_features % 8 == 0"
+                print(f"linear: {fqn}, in={in_features}, out={out_features}")
+
+                weight = mod.weight.data
+                if not _int4_check_linear_int4_k(in_features, self.groupsize, self.inner_k_tiles):
+                    if self.padding_allowed:
+                        from model import find_multiple
+                        import torch.nn.functional as F
+                        print(f"warning: {fqn} is padded to satisfy in_features % 1024 == 0")
+                        padded_in_features = find_multiple(in_features, 1024)
+                        weight = F.pad(weight, pad=(0, padded_in_features - in_features))
+                    else:
+                        print(f"warning: {fqn} is skipped, int4 requires that in_features is 32, 64, or is divisible by 1024, " +
+                            "and that groupsize and inner_k_tiles*16 evenly divide into it")
+                        continue
+                weight_int4pack, scales_and_zeros = _int4_prepare_int4_weight_and_scales_and_zeros(
+                    weight.to(torch.float), self.groupsize, self.inner_k_tiles
+                )
+                cur_state_dict[f"{fqn}.weight"] = weight_int4pack.to('cpu')
+                cur_state_dict[f"{fqn}.scales_and_zeros"] = scales_and_zeros.to('cpu')
+
+        return cur_state_dict
+
+    def convert_for_runtime(self, use_cuda=False):
+        replace_linear_int4(self.mod, self.groupsize, self.inner_k_tiles, self.padding_allowed, use_cuda)
+        return self.mod
+
+    def quantized_model(self) -> nn.Module:
+        model_updated_state_dict = self.create_quantized_state_dict()
+        self.convert_for_runtime()
+        self.mod.load_state_dict(model_updated_state_dict)
+        return self.mod
+
+
+class WeightOnlyInt4Linear(torch.nn.Module):
+    __constants__ = ['in_features', 'out_features']
+    in_features: int
+    out_features: int
+    weight: torch.Tensor
+
+    def __init__(
+            self, in_features: int, out_features: int,
+            bias=True, device=None, dtype=None, groupsize: int = 128, inner_k_tiles: int = 8, use_cuda=True,
+    ) -> None:
+        super().__init__()
+        self.padding = not _int4_check_linear_int4_k(in_features, groupsize, inner_k_tiles)
+        if self.padding:
+            from model import find_multiple
+            self.origin_in_features = in_features
+            in_features = find_multiple(in_features, 1024)
+
+        self.in_features = in_features
+        self.out_features = out_features
+        assert not bias, "require bias=False"
+        self.groupsize = groupsize
+        self.inner_k_tiles = inner_k_tiles
+
+        assert out_features % 8 == 0, "require out_features % 8 == 0"
+        assert in_features % (inner_k_tiles * 16) == 0, "require in_features % (innerKTiles * 16) == 0"
+        self.register_buffer(
+            "weight",
+            torch.empty((out_features // 8, in_features // (inner_k_tiles * 16), 32, inner_k_tiles // 2), dtype=torch.int32)
+        )
+        # MKG: torch.float
+        self.register_buffer(
+            "scales_and_zeros",
+            torch.empty((in_features // groupsize, out_features, 2), dtype=torch.float)
+        )
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # MKG torch.float
+        input = input.to(torch.float)
+        if self.padding:
+            import torch.nn.functional as F
+            input = F.pad(input, pad=(0, self.in_features - self.origin_in_features))
+        return linear_forward_int4(
+            input,
+            self.weight, self.scales_and_zeros, self.out_features, self.groupsize
+        )
+
+########################################################################
+### Int8 Dynamic Activations 4 Bit Weights
 
 def prepare_int4_weight_and_scales_and_zeros(weight, group_size, precision):
     weight_int8, scales, zeros = group_quantize_tensor_symmetric(
@@ -523,7 +786,6 @@ def find_multiple(n: int, *args: Tuple[int]) -> int:
 def _check_linear_int4_k(k, group_size=1):
     return k % group_size == 0
 
-
 def _calc_padded_size_linear_int4(k, groupsize=1):
     return find_multiple(k, groupsize)
 
@@ -560,7 +822,7 @@ def replace_linear_8da4w(
             )
 
 
-class Int8DynActInt4WeightQuantHandler:
+class Int8DynActInt4WeightQuantHandler(QuantHandler):
     def __init__(
         self,
         mod,