diff --git a/py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py b/py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py
index e46b6863a3..5c6943bf5b 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py
@@ -149,6 +149,8 @@ def native_group_norm(
     eps: float,
     return_mean_rstd: bool = True,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    # TODO: Ask TRT team about the usage of INormalization Layer usage with num_groups and update the implementation
+    #       with INormalization Layer
     assert (
         len(input.shape) >= 3
     ), f"The input dimension should not be less than 3, got {len(input.shape)}!"
@@ -187,28 +189,105 @@ def native_group_norm(
         shape,
     )
 
+    if weight is None:
+        weight = to_numpy(1.0)
+
+    if bias is None:
+        bias = to_numpy(0.0)
+
     weight = get_trt_tensor(ctx, weight, f"{name}_weight")
     bias = get_trt_tensor(ctx, bias, f"{name}_bias")
-    if tuple(reshaped_input.shape) != tuple(weight.shape):
-        weight = impl.slice.expand(
-            ctx,
-            target,
-            source_ir,
-            f"{name}_expand_weight",
-            weight,
-            reshaped_input.shape,
-        )
-    if tuple(reshaped_input.shape) != tuple(bias.shape):
-        bias = impl.slice.expand(
-            ctx, target, source_ir, f"{name}_expand_bias", bias, reshaped_input.shape
-        )
+    weight_bias_shape = (1, C) + (1,) * (len(input.shape) - 2)
+
     dims = list(range(1, len(input.shape)))
-    axes = get_axes_for_reduce_op(dims)
-    group_norm = ctx.net.add_normalization(reshaped_input, weight, bias, axes)
-    group_norm.epsilon = eps
-    group_norm.compute_precision = input.dtype
-    set_layer_name(group_norm, target, f"{name}_group_norm", source_ir)
-    output = group_norm.get_output(0)
+
+    # E[X]
+    mean_trt = impl.reduce.mean(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_mean",
+        reshaped_input,
+        dims,
+        True,
+    )
+
+    mean_trt = impl.slice.expand(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_expand_mean_trt",
+        mean_trt,
+        reshaped_input.shape,
+    )
+
+    # X - E[X]
+    sub_trt = impl.elementwise.sub(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_sub",
+        reshaped_input,
+        mean_trt,
+    )
+
+    # variance
+    pow_trt = get_trt_tensor(ctx, 2, f"{name}_power", np.float32)
+    pow_var = impl.elementwise.pow(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_pow",
+        sub_trt,
+        pow_trt,
+    )
+
+    var_trt = impl.reduce.mean(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_mean_var",
+        pow_var,
+        dims,
+        True,
+    )
+
+    var_trt = impl.slice.expand(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_expand_var_trt",
+        var_trt,
+        reshaped_input.shape,
+    )
+
+    eps_trt = get_trt_tensor(ctx, eps, f"{name}_eps", np.float32)
+    add_trt = impl.elementwise.add(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_add",
+        var_trt,
+        eps_trt,
+    )
+
+    sqrt_trt = impl.unary.sqrt(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_sqrt",
+        add_trt,
+    )
+
+    # y = (X - E[X]) / sqrt((var + eps))
+    output = impl.elementwise.div(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_div",
+        sub_trt,
+        sqrt_trt,
+    )
 
     shape = list(output.shape)
     for i, s in enumerate(shape):
@@ -222,6 +301,40 @@ def native_group_norm(
     reshaped_output = impl.shuffle.reshape(
         ctx, target, source_ir, f"{name}_reshape_output", output, shape
     )
+    reshaped_gamma = impl.shuffle.reshape(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_reshape_gamma",
+        weight,
+        weight_bias_shape,
+    )
+
+    reshaped_output = impl.elementwise.mul(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_mul_gamma",
+        reshaped_output,
+        reshaped_gamma,
+    )
+
+    reshaped_bias = impl.shuffle.reshape(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_reshape_beta",
+        bias,
+        weight_bias_shape,
+    )
+    reshaped_output = impl.elementwise.add(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_add_beta",
+        reshaped_output,
+        reshaped_bias,
+    )
     if return_mean_rstd:
         # return fake mean and rstd for now
         return reshaped_output, None, None
diff --git a/tests/py/dynamo/conversion/test_group_norm_aten.py b/tests/py/dynamo/conversion/test_group_norm_aten.py
index 6ac782cdc7..cf5dd48f0a 100644
--- a/tests/py/dynamo/conversion/test_group_norm_aten.py
+++ b/tests/py/dynamo/conversion/test_group_norm_aten.py
@@ -31,8 +31,8 @@ def forward(self, x):
                 return torch.ops.aten.group_norm.default(
                     x,
                     2,
-                    torch.ones((6,)),
-                    torch.zeros((6,)),
+                    torch.randn((6,)),
+                    torch.randn((6,)),
                     1e-05,
                     True,
                 )
@@ -50,8 +50,8 @@ def forward(self, x):
                 return torch.ops.aten.group_norm.default(
                     x,
                     2,
-                    torch.ones((6,)),
-                    torch.zeros((6,)),
+                    torch.randn((6,)),
+                    torch.randn((6,)),
                     1e-05,
                     True,
                 )
@@ -112,6 +112,27 @@ def forward(self, x):
                 inputs,
             )
 
+    def test_groupnorm_sd(self):
+        class GroupNorm(torch.nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.native_group_norm.default(
+                    x,
+                    torch.randn((320,)).half(),
+                    torch.randn((320,)).half(),
+                    2,
+                    320,
+                    4096,
+                    32,
+                    1e-05,
+                )[0]
+
+        inputs = [torch.randn(2, 320, 64, 64).half()]
+        with torch.no_grad():
+            self.run_test(
+                GroupNorm(),
+                inputs,
+            )
+
     @parameterized.expand(
         [
             (5, 4, 4, 2, (2, 4, 2), (3, 4, 2), (5, 4, 4)),