diff --git a/tests/quantization/test_torchao.py b/tests/quantization/test_torchao.py
index eef3568efea1..c84608fe6f2f 100644
--- a/tests/quantization/test_torchao.py
+++ b/tests/quantization/test_torchao.py
@@ -75,5 +75,20 @@ def test_qwenvl_int8wo_model_loading_with_params(vllm_runner):
         print(output)
 
 
+@pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available")
+def test_phi4mini_int4wo_awq_model_loading_with_params(vllm_runner):
+    torch._dynamo.reset()
+    model_name = "torchao-testing/Qwen3-4B-int4wo-awq-0.13-dev"
+    with vllm_runner(model_name=model_name,
+                     quantization="torchao",
+                     dtype="bfloat16",
+                     pt_load_map_location="cuda:0") as llm:
+        output = llm.generate_greedy(["The capital of France is"],
+                                     max_tokens=32)
+
+        assert output
+        print(output)
+
+
 if __name__ == "__main__":
     pytest.main([__file__])
diff --git a/vllm/model_executor/layers/quantization/torchao.py b/vllm/model_executor/layers/quantization/torchao.py
index 63b2ab6bab06..3498d2994c2a 100644
--- a/vllm/model_executor/layers/quantization/torchao.py
+++ b/vllm/model_executor/layers/quantization/torchao.py
@@ -152,18 +152,20 @@ def torchao_quantize_param_data(param: torch.Tensor,
     from torchao.quantization import quantize_
 
     assert isinstance(torchao_config, AOBaseConfig), f"{torchao_config}"
-    """ 
-    Avoid real weight allocation for faster load, since we will 
+    """
+    Avoid real weight allocation for faster load, since we will
     end up setting it to param.
     """
     with torch.device("meta"):
-        dummy_linear = torch.nn.Linear(param.shape[1],
-                                       param.shape[0],
-                                       bias=False)
+        # linear can't be top level module since quantize_ is inplace
+        # while some of our configs need to do module swap, and only non-top
+        # level modules support module swap
+        dummy_linear = torch.nn.Sequential(
+            torch.nn.Linear(param.shape[1], param.shape[0], bias=False))
 
-    dummy_linear.weight = param
+    dummy_linear[0].weight = param
     quantize_(dummy_linear, torchao_config)
-    return dummy_linear.weight
+    return dummy_linear[0].weight
 
 
 class TorchAOLinearMethod(LinearMethodBase):