diff --git a/torchchat/utils/quantize.py b/torchchat/utils/quantize.py
index 31c639dfd..6fa8c2e39 100644
--- a/torchchat/utils/quantize.py
+++ b/torchchat/utils/quantize.py
@@ -36,6 +36,7 @@
 from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib  # noqa
 from torchao.quantization.quant_api import (
     int4_weight_only,
+    int8_weight_only,
     Int4WeightOnlyQuantizer,
     Int8DynActInt4WeightQuantizer,
     quantize_,
@@ -110,12 +111,20 @@ def quantize_model(
         if quantizer not in quantizer_class_dict:
             raise RuntimeError(f"unknown quantizer {quantizer} specified")
         else:
+            ao_quant = True
             # Use tensor subclass API for int4 weight only.
             if device == "cuda" and quantizer == "linear:int4":
                 quantize_(model, int4_weight_only(q_kwargs["groupsize"]))
+            elif quantizer == "linear:int8":
+                print("quantizer is linear int8")
+                quantize_(model, int8_weight_only())
+            else: 
+                ao_quant = False
+            if ao_quant:
                 if not support_tensor_subclass:
                     unwrap_tensor_subclass(model)
                 continue
+                
             
             if quantizer in ["linear:a8wxdq", "embedding:wx"]:
                 # These quantizers require float32 input weights.  Note that after quantization,
@@ -529,147 +538,6 @@ def linear_int8_et(input, weight, scales):
     )
 
 
-class WeightOnlyInt8Linear(nn.Module):
-    __constants__ = ["in_features", "out_features"]
-    in_features: int
-    out_features: int
-    weight: torch.Tensor
-    scales: torch.Tensor
-
-    def __init__(
-        self,
-        in_features,
-        out_features,
-        bias=None,
-        device=None,
-        dtype=None,
-        *,
-        weight: Optional[torch.Tensor] = None,
-        scales: Optional[torch.Tensor] = None,
-        groupsize: Optional[int] = None,
-    ):
-        super().__init__()
-        if dtype is None:
-            dtype = torch.get_default_dtype()
-
-        if device is None:
-            device = "cpu"
-
-        assert not bias, "Bias is not supported by LinearInt8"
-        self.in_features = in_features
-        self.out_features = out_features
-
-        assert (weight is None) == bool(
-            scales is None
-        ), "must specify both weights and scales, or neither"
-        if weight is None:
-            weight = torch.empty(
-                (out_features, in_features),
-                dtype=torch.int8,
-                device=device,
-            )
-            if groupsize is None or (groupsize == 0):
-                scales = torch.empty(out_features, dtype=dtype, device=device)
-            else:
-                n_groups = (in_features + groupsize - 1) // groupsize
-                scales = torch.empty(out_features, n_groups, dtype=dtype, device=device)
-
-        self.register_buffer("weight", weight.to(device))
-        self.register_buffer("scales", scales.to(device))
-
-        if use_et_backend():
-            self.forward = self.et_forward
-        else:
-            self.forward = self.aoti_forward
-
-    def aoti_forward(self, input: torch.Tensor) -> torch.Tensor:
-        return linear_int8_aoti(input, self.weight, self.scales)
-
-    def et_forward(self, input: torch.Tensor) -> torch.Tensor:
-        return linear_int8_et(input, self.weight, self.scales)
-
-
-class WeightOnlyInt8QuantHandler(QuantHandler):
-    def __init__(
-        self,
-        model: Optional[nn.Module] = None,
-        device = None,
-        precision=None,
-        tokenizer=None,
-        *,
-        node_type: str = "*",
-        bitwidth: Optional[int] = None,
-        groupsize: Optional[int] = None,
-    ):
-        self.model_ = model
-        self.device = device
-        self.groupsize = groupsize
-        self.node_type = node_type
-        if bitwidth is None:
-            self.bitwidth = 8
-        else:
-            self.bitwidth = bitwidth
-
-    @torch.no_grad()
-    def quantize(self, module):
-        # cur_state_dict = state_dict_device(self.model_.state_dict())
-        # dict_device = "cpu"  # self.device
-
-        if self.bitwidth == 4:
-            range_min = -8
-            range_max = 7
-        elif self.bitwidth == 8:
-            range_min = -128
-            range_max = 127
-        else:
-            raise ValueError(f"Unsupported bitwidth {self.bitwidth}")
-
-        for name, child in module.named_children():
-            # print(f"name: {name}")
-            if isinstance(child, nn.Linear):
-                if (
-                    (self.node_type == "*")
-                    or (self.node_type == "output" and name == "output")
-                    or (self.node_type == "!output" and name != "output")
-                ):
-                    # print(f"{name, child}")
-                    input_weight = child.weight.float()
-                    # print(f"{name, child}")
-                    # print(f"in_features: {child.in_features}")
-                    # print(f"out_features: {child.out_features}")
-
-                    # print(f"expanded weight shape {input_weight.shape}")
-                    weight, scales, _ = dynamically_quantize_per_channel(
-                        input_weight,
-                        range_min,
-                        range_max,
-                        torch.int8,
-                        self.groupsize,
-                        scales_dtype=child.weight.dtype,
-                    )
-
-                    setattr(
-                        module,
-                        name,
-                        WeightOnlyInt8Linear(
-                            in_features=child.in_features,
-                            out_features=child.out_features,
-                            device=self.device,
-                            # update variables from quantization
-                            weight=weight,
-                            scales=scales,
-                            groupsize=self.groupsize,
-                        ),
-                    )
-            else:
-                self.quantize(child)
-
-        return module
-
-    def quantized_model(self) -> nn.Module:
-        return self.quantize(self.model_)
-
-
 #########################################################################
 #####                   embedding table quantization               ######
 ###                    (unify with torchao in future)                 ###
@@ -886,10 +754,10 @@ def quantized_model(self) -> nn.Module:
 # class references
 quantizer_class_dict = {
     "embedding": EmbeddingOnlyQuantHandler,
-    "linear:int8": WeightOnlyInt8QuantHandler,
     "precision": PrecisionHandler,
     "executor": ExecutorHandler,
     "linear:int4": Int4WeightOnlyQuantizer,
+    "linear:int8": int8_weight_only,
     "linear:a8w4dq": Int8DynActInt4WeightQuantizer,
 }
 
@@ -917,6 +785,7 @@ def quantized_model(self) -> nn.Module:
         IntxWeightEmbeddingQuantizer,
     )
 
+
     quantizer_class_dict["linear:a8wxdq"] = Int8DynActIntxWeightLinearQuantizer
     quantizer_class_dict["embedding:wx"] = IntxWeightEmbeddingQuantizer