diff --git a/python_module/megengine/module/linear.py b/python_module/megengine/module/linear.py
index ff4da3b5..30f1ec3d 100644
--- a/python_module/megengine/module/linear.py
+++ b/python_module/megengine/module/linear.py
@@ -8,13 +8,13 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import numpy as np
 
+from .. import functional as F
 from ..core import Parameter
-from ..functional import linear
 from . import init
-from .module import Module
+from .module import QATModule
 
 
-class Linear(Module):
+class Linear(QATModule):
     r"""Applies a linear transformation to the input. For instance, if input
     is x, then output y is:
 
@@ -55,5 +55,18 @@ class Linear(Module):
         if self.bias is not None:
             init.zeros_(self.bias)
 
+    def _calc_linear(self, x, weight, bias):
+        return F.linear(x, weight, bias)
+
     def forward(self, x):
-        return linear(x, self.weight, self.bias)
+        return self._calc_linear(x, self.weight, self.bias)
+
+    def forward_qat(self, x):
+        w_qat = self.apply_fakequant_with_observer(
+            self.weight, self.weight_fake_quant, self.weight_observer
+        )
+        return self.apply_fakequant_with_observer(
+            self._calc_linear(x, w_qat, self.bias),
+            self.act_fake_quant,
+            self.act_observer,
+        )
diff --git a/python_module/megengine/module/quantized/__init__.py b/python_module/megengine/module/quantized/__init__.py
index c7c9635e..040a3b14 100644
--- a/python_module/megengine/module/quantized/__init__.py
+++ b/python_module/megengine/module/quantized/__init__.py
@@ -8,4 +8,5 @@
 from .concat import Concat
 from .conv_bn_relu import ConvBn2d, ConvBnRelu2d
 from .elemwise import Elemwise
+from .linear import Linear
 from .quant_dequant import DequantStub, QuantStub
diff --git a/python_module/megengine/module/quantized/linear.py b/python_module/megengine/module/quantized/linear.py
new file mode 100644
index 00000000..243db7d7
--- /dev/null
+++ b/python_module/megengine/module/quantized/linear.py
@@ -0,0 +1,60 @@
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import numpy as np
+
+import megengine._internal as mgb
+
+from ... import functional as F
+from ... import module as Float
+from ...core import Parameter
+from ...quantization.utils import register_method_to_class
+from ..module import Module
+
+
+class Linear(Module):
+    r"""Applies a quantized linear transformation to the input. The module
+    usually convert from QAT module by to_quantized method.
+
+    :param dtype: output data type.
+
+    """
+
+    def __init__(
+        self, dtype: np.dtype = None,
+    ):
+        super().__init__()
+        self.weight = None
+        self.bias = None
+        self.output_dtype = dtype
+
+    def forward(self, inp):
+        if self.training:
+            raise ValueError("quantized module only support inference.")
+        inp_scale = mgb.dtype.get_scale(inp.dtype)
+        w_scale = mgb.dtype.get_scale(self.weight.dtype)
+        bias_dtype = mgb.dtype.qint32(inp_scale * w_scale)
+        return F.linear(
+            inp,
+            self.weight,
+            None if self.bias is None else self.bias.astype(bias_dtype),
+        ).astype(self.output_dtype)
+
+
+@register_method_to_class(Float.Linear)
+def to_quantized(float_module):
+    r"""
+    Replace :class:`~.module.QATModule`'s ``to_quantized`` method.
+    implemented here to avoid circular import.
+    """
+    output_dtype = float_module.act_observer.get_dtype()
+    qmod = Linear(dtype=output_dtype,)
+    weight = float_module.weight.astype(float_module.weight_observer.get_dtype())
+    qmod.weight = Parameter(weight.numpy())
+    if float_module.bias is not None:
+        qmod.bias = Parameter(float_module.bias.numpy())
+    return qmod