diff --git a/modelopt/onnx/autocast/precisionconverter.py b/modelopt/onnx/autocast/precisionconverter.py
index 8b6fd545d..4d6917fd2 100644
--- a/modelopt/onnx/autocast/precisionconverter.py
+++ b/modelopt/onnx/autocast/precisionconverter.py
@@ -179,13 +179,16 @@ def convert(
             for vi in self.model.graph.value_info:
                 vi.type.tensor_type.elem_type = onnx.TensorProto.UNDEFINED
                 for idx, d in enumerate(vi.type.tensor_type.shape.dim):
-                    vi.type.tensor_type.shape.dim[idx].dim_param = "unk"
+                    if d.dim_value:
+                        vi.type.tensor_type.shape.dim[idx].dim_param = "unk"
             for out in self.model.graph.output:
                 out.type.tensor_type.elem_type = onnx.TensorProto.UNDEFINED
                 for idx, d in enumerate(out.type.tensor_type.shape.dim):
-                    out.type.tensor_type.shape.dim[idx].dim_param = "unk"
+                    if d.dim_value:
+                        out.type.tensor_type.shape.dim[idx].dim_param = "unk"
             # Populate type information with inferred types
             self.model = onnx_utils.infer_shapes(self.model, strict_mode=True, check_type=False)
+            self._ensure_types_are_defined()
             # Sanity check: Verify type correctness
             self.model = onnx_utils.infer_shapes(self.model, strict_mode=True, check_type=True)
 
@@ -201,6 +204,12 @@ def convert(
 
         return self.model
 
+    def _ensure_types_are_defined(self):
+        """Ensure that all tensor types are defined."""
+        for vi in self.model.graph.value_info:
+            if vi.type.tensor_type.elem_type == onnx.TensorProto.UNDEFINED:
+                vi.type.tensor_type.elem_type = self.low_precision_type.onnx_type
+
     def _propagate_types_shapes_custom_ops(self, model):
         """Propagate types and shapes after insertion of 'Cast' nodes or other graph modifications."""
         logger.info("Propagating tensor shapes and types in model with custom ops.")