diff --git a/neural_compressor/adaptor/torch_utils/weight_only.py b/neural_compressor/adaptor/torch_utils/weight_only.py
index 53ea98251a8..8a1a683483f 100644
--- a/neural_compressor/adaptor/torch_utils/weight_only.py
+++ b/neural_compressor/adaptor/torch_utils/weight_only.py
@@ -176,7 +176,7 @@ def qdq_weight_sym(weight, num_bits=4, quantile=1.0, return_int=False, full_rang
     weight.round_()
     weight.clamp_(minq, maxq)
     if return_int:
-        return weight, scale.type(torch.float), None
+        return weight, scale, None
     return weight.mul_(scale)
 
 
@@ -238,6 +238,7 @@ def quant_weight(
 
     orig_shape = weight.shape
     if weight.shape[1] % group_size == 0:
+        orig_weight = weight
         weight = weight.reshape(-1, group_size)
         if return_int:
             weight, scale, zp = qdq_weight_actor(
@@ -250,17 +251,21 @@ def quant_weight(
                 data_type=data_type,
             )
             weight = weight.reshape(orig_shape)
+            orig_weight.copy_(weight)
             scale = scale.reshape(orig_shape[0], -1)
             if zp is not None:
                 zp = zp.reshape(orig_shape[0], -1)
-            return weight, scale, zp
+            return orig_weight, scale, zp
         else:
             qdq_weight_actor(
                 weight, num_bits, scheme=scheme, data_type=data_type, quantile=quantile, full_range=full_range
             )
-            return weight.reshape(orig_shape)
+            weight = weight.reshape(orig_shape)
+            orig_weight.copy_(weight)
+            return orig_weight
     else:
         split_index = weight.shape[1] // group_size * group_size
+        orig_weight = weight
         weight1 = weight[:, :split_index]
         weight1 = weight1.reshape(-1, group_size)
         if return_int:
@@ -277,7 +282,7 @@ def quant_weight(
             if zp1 is not None:
                 zp1 = zp1.reshape(orig_shape[0], -1)
         else:
-            weight1 = qdq_weight_actor(
+            qdq_weight_actor(
                 weight1, num_bits, scheme=scheme, quantile=quantile, data_type=data_type, full_range=full_range
             )
         weight1 = weight1.reshape(orig_shape[0], split_index)
@@ -292,19 +297,19 @@ def quant_weight(
                 return_int=True,
                 full_range=full_range,
             )
-            weight.copy_(torch.cat([weight1, weight2], dim=1))
+            orig_weight.copy_(torch.cat([weight1, weight2], dim=1))
             scale = torch.cat([scale1, scale2], dim=1)
             if zp2 is not None:
                 zp = torch.cat([zp1, zp2], dim=1)
             else:
                 zp = None
-            return weight, scale, zp
+            return orig_weight, scale, zp
         else:
             weight2 = qdq_weight_actor(
                 weight2, num_bits, scheme=scheme, data_type=data_type, quantile=quantile, full_range=full_range
             )
-            weight.copy_(torch.cat([weight1, weight2], dim=1))
-            return weight
+            orig_weight.copy_(torch.cat([weight1, weight2], dim=1))
+            return orig_weight
 
 
 def search_clip(m, num_bits=4, group_size=32, scheme="asym", data_type="int", enable_full_range=False):
diff --git a/neural_compressor/torch/algorithms/weight_only/utility.py b/neural_compressor/torch/algorithms/weight_only/utility.py
index 1fd98d82715..0029016459f 100644
--- a/neural_compressor/torch/algorithms/weight_only/utility.py
+++ b/neural_compressor/torch/algorithms/weight_only/utility.py
@@ -266,8 +266,10 @@ def quant_tensor(
         group_size = weight.shape[1]
     # case 2, reshape based on group size
     orig_shape = weight.shape
+    orig_weight = weight
     if weight.shape[1] % group_size == 0:
         weight = weight.reshape(-1, group_size)
+        # return weight for unpacking scale and zp
         weight = qdq_weight_actor(
             weight,
             bits,
@@ -281,12 +283,15 @@ def quant_tensor(
         if return_int or quant_scale:
             weight, scale, zp = weight
             weight = weight.reshape(orig_shape)
+            orig_weight.copy_(weight)
             scale = scale.reshape(orig_shape[0], -1)
             if zp is not None:
                 zp = zp.reshape(orig_shape[0], -1)
-            q_state = weight, scale, zp
+            q_state = orig_weight, scale, zp
         else:
-            return weight.reshape(orig_shape)
+            weight = weight.reshape(orig_shape)
+            orig_weight.copy_(weight)
+            return orig_weight
     else:
         # case 3, process left part split by group size
         split_index = weight.shape[1] // group_size * group_size
@@ -321,13 +326,13 @@ def quant_tensor(
         )
         if return_int or quant_scale:
             weight2, scale2, zp2 = weight2
-            weight.copy_(torch.cat([weight1, weight2], dim=1))
+            orig_weight.copy_(torch.cat([weight1, weight2], dim=1))
             scale = torch.cat([scale1, scale2], dim=1)
             zp = None if zp2 is None else torch.cat([zp1, zp2], dim=1)
             q_state = (weight, scale, zp)
         else:
-            weight.copy_(torch.cat([weight1, weight2], dim=1))
-            return weight
+            orig_weight.copy_(torch.cat([weight1, weight2], dim=1))
+            return orig_weight
     if quant_scale:
         weight, scale, zp = q_state
         scale_dtype = kwargs.get("double_quant_dtype", "int")
@@ -343,7 +348,7 @@ def quant_tensor(
             scale.sub_(scale_mean)
             scale_scheme = "sym"
         # process: scale
-        scale = quant_tensor(
+        quant_tensor(
             scale,
             dtype=scale_dtype,
             bits=scale_bits,
@@ -375,7 +380,7 @@ def quant_tensor(
                 weight1 = weight1.mul_(scale[:, :-1].reshape(-1, 1))
                 weight1 = weight1.reshape(orig_shape[0], -1)
                 weight2 = weight2.mul_(scale[:, -1].reshape(-1, 1))
-                weight.copy_(torch.cat([weight1, weight2], dim=1))
+                orig_weight.copy_(torch.cat([weight1, weight2], dim=1))
             else:
                 if zp is not None:
                     weight = weight.reshape(-1, group_size) - zp.reshape(-1, 1)
@@ -383,7 +388,8 @@ def quant_tensor(
                     weight = weight.reshape(-1, group_size)
                 weight = weight.mul_(scale.reshape(-1, 1))
                 weight = weight.reshape(orig_shape[0], -1)
-            return weight
+                orig_weight.copy_(weight)
+            return orig_weight
     else:
         return q_state
 
diff --git a/test/3x/torch/algorithms/weight_only/test_woq_utility.py b/test/3x/torch/algorithms/weight_only/test_woq_utility.py
new file mode 100644
index 00000000000..f672ec0ac1c
--- /dev/null
+++ b/test/3x/torch/algorithms/weight_only/test_woq_utility.py
@@ -0,0 +1,13 @@
+import pytest
+import torch
+
+
+@pytest.mark.parametrize("shape", [1024, 512, 300])
+def test_quant_tensor_id(shape):
+    from neural_compressor.torch.algorithms.weight_only.utility import quant_tensor
+
+    input = torch.randn(shape, shape)
+    id1 = id(input)
+    output = quant_tensor(input)
+    id2 = id(output)
+    assert id1 == id2, "quant_tensor function is an in-place operator"