diff --git a/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py b/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py
index 286650c77fd..ded746fe786 100644
--- a/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py
+++ b/tensorrt_llm/_torch/auto_deploy/utils/node_utils.py
@@ -304,6 +304,16 @@ def is_any_lin_op(node: Node) -> bool:
     return is_linear_op(node) or is_fake_quantized_linear_op(node)
 
 
+def is_fp4_op(node: Node) -> bool:
+    return is_op(
+        node,
+        [
+            torch.ops.auto_deploy.torch_quant_nvfp4_linear,
+            torch.ops.auto_deploy.torch_fake_quant_nvfp4_linear,
+        ],
+    )
+
+
 def is_any_moe_op(node: Node) -> bool:
     return is_op(
         node,
@@ -733,16 +743,20 @@ def boundary_condition(n):
     return subgraph_nodes
 
 
-def get_weight_shape(
-    node: Node, dim: Optional[int] = None
-) -> Optional[Union[int, Tuple[int, ...]]]:
+def get_weight_shape(node: Node, dim: Optional[int] = None) -> Optional[Union[int, List[int]]]:
     """Get the shape of the weight node."""
     if not is_any_lin_op(node):
         return None
+    s = list(shape(extract_weight_node(node)))
+    if len(s) == 0:
+        return None
+    if is_fp4_op(node):
+        # FP4 weights are packed as uint8 type with 2 FP4 values per element
+        s[-1] *= 2
     if dim is None:
-        return shape(extract_weight_node(node))
+        return s
     else:
-        return shape(extract_weight_node(node))[dim]
+        return s[dim]
 
 
 def get_layer_after_linear_node(