diff --git a/test/quantization/test_quant_api.py b/test/quantization/test_quant_api.py
index f3e2852a65..fce977a971 100644
--- a/test/quantization/test_quant_api.py
+++ b/test/quantization/test_quant_api.py
@@ -77,6 +77,7 @@
 from torchao.prototype.mx_formats.inference_workflow import (
     MXDynamicActivationMXWeightConfig,
     NVFP4DynamicActivationNVFP4WeightConfig,
+    NVFP4WeightOnlyConfig,
 )
 
 
@@ -1061,6 +1062,7 @@ def test_fqn_to_config_non_weight_param(self):
             configs.append(MXDynamicActivationMXWeightConfig())
         if is_sm_at_least_100() and torch_version_at_least("2.8.0"):
             configs.append(NVFP4DynamicActivationNVFP4WeightConfig())
+            configs.append(NVFP4WeightOnlyConfig())
         for config in configs:
             with self.subTest(config=type(config).__name__):
                 model = torch.nn.Sequential(
diff --git a/torchao/prototype/mx_formats/inference_workflow.py b/torchao/prototype/mx_formats/inference_workflow.py
index ce2ecbaae0..447e5229e5 100644
--- a/torchao/prototype/mx_formats/inference_workflow.py
+++ b/torchao/prototype/mx_formats/inference_workflow.py
@@ -377,10 +377,13 @@ def __post_init__(self):
 
 @register_quantize_module_handler(NVFP4WeightOnlyConfig)
 def _nvfp4_weight_only_linear_transform(
-    module: torch.nn.Linear, config: NVFP4WeightOnlyConfig
+    module: torch.nn.Linear,
+    config: NVFP4WeightOnlyConfig,
+    *,
+    parameter_name: str = "weight",
 ):
     """Quantization handler for NVFP4WeightOnlyConfig"""
-    weight = module.weight
+    weight = getattr(module, parameter_name)
 
     if weight.shape[-2] % 16 != 0 or weight.shape[-1] % 16 != 0:
         raise RuntimeError(
@@ -399,8 +402,19 @@ def _nvfp4_weight_only_linear_transform(
         act_quant_kwargs=None,
     )
     # Set triton preference after construction
-    module.weight = torch.nn.Parameter(quantized_weight, requires_grad=False)
-    module.extra_repr = types.MethodType(_linear_extra_repr, module)
+    setattr(
+        module,
+        parameter_name,
+        torch.nn.Parameter(quantized_weight, requires_grad=False),
+    )
+    module.extra_repr = types.MethodType(
+        partial(
+            _module_extra_repr,
+            original_extra_repr=module.extra_repr,
+            parameter_name=parameter_name,
+        ),
+        module,
+    )
     return module