diff --git a/torchao/prototype/mx_formats/inference_workflow.py b/torchao/prototype/mx_formats/inference_workflow.py
index 1b9c369be5..cc6e34a708 100644
--- a/torchao/prototype/mx_formats/inference_workflow.py
+++ b/torchao/prototype/mx_formats/inference_workflow.py
@@ -24,6 +24,7 @@
     QuantizeTensorToNVFP4Kwargs,
     per_tensor_amax_to_scale,
 )
+from torchao.quantization.quant_api import _quantization_type
 from torchao.quantization.transform_module import (
     register_quantize_module_handler,
 )
@@ -89,7 +90,7 @@ def __post_init__(self):
 
 
 def _linear_extra_repr(self):
-    return f"in_features={self.weight.shape[1]}, out_features={self.weight.shape[0]}, weight={repr(self.weight)}"
+    return f"in_features={self.weight.shape[1]}, out_features={self.weight.shape[0]}, weight={_quantization_type(self.weight)}"
 
 
 @register_quantize_module_handler(MXFPInferenceConfig)
diff --git a/torchao/prototype/mx_formats/mx_tensor.py b/torchao/prototype/mx_formats/mx_tensor.py
index 8532337477..0235df1b5a 100644
--- a/torchao/prototype/mx_formats/mx_tensor.py
+++ b/torchao/prototype/mx_formats/mx_tensor.py
@@ -544,6 +544,9 @@ def __repr__(self):
         # TODO better elem dtype print for fp4
         return f"MXTensor: elem_dtype: {self._elem_dtype}, s_e8m0: {self._scale_e8m0}, d: {self.qdata}, act_quant_kwargs: {self.act_quant_kwargs}"  # noqa: E501
 
+    def _quantization_type(self):
+        return f"{self._elem_dtype=}, {self._block_size=}, {self._orig_dtype=}, {self._gemm_kernel_choice=}, {self.act_quant_kwargs=}"
+
     @classmethod
     def __torch_dispatch__(cls, func, types, args, kwargs=None):
         # avoid circular dependency