diff --git a/examples/models/qwen3_5_moe/export.py b/examples/models/qwen3_5_moe/export.py
index f4feb6fae10..8c544dc28c5 100644
--- a/examples/models/qwen3_5_moe/export.py
+++ b/examples/models/qwen3_5_moe/export.py
@@ -196,6 +196,12 @@ def load_and_quantize(args):  # noqa: C901
         # CUDA: quantize experts with packed INT4 for Triton kernel
         if args.qlinear or args.qembedding:
             _quantize(model, config, args)
+            # Unwrap torchao tensor subclasses (e.g. AffineQuantizedTensor)
+            # into parametrized plain tensors so torch.export can handle them.
+            # Mirrors executorch/export/stages.py SourceTransformStage.
+            from torchao.utils import unwrap_tensor_subclass
+
+            unwrap_tensor_subclass(model)
         else:
             model.to(dtype=torch.bfloat16)
 
@@ -290,6 +296,14 @@ def load_prequantized_model(prequantized_dir, max_seq_len=4096, use_splitk_decod
     # requires_grad=True, which fails. Disable grad on all parameters.
     for p in model.parameters():
         p.requires_grad_(False)
+
+    # Unwrap any torchao tensor subclasses (e.g. AffineQuantizedTensor) into
+    # parametrized plain tensors so torch.export can handle them. Mirrors the
+    # canonical pattern in executorch/export/stages.py SourceTransformStage.
+    from torchao.utils import unwrap_tensor_subclass
+
+    unwrap_tensor_subclass(model)
+
     model.eval()
 
     print(
@@ -966,6 +980,9 @@ def main():  # noqa: C901
         # Register FLA Triton kernel (CUDA only)
         import executorch.backends.cuda.triton.kernels  # noqa: F401
 
+        if torch.cuda.is_available():
+            torch.cuda.reset_peak_memory_stats()
+
     if args.backend == "mlx":
         if args.prequantized:
             parser.error("--prequantized is not supported with --backend mlx")
@@ -988,6 +1005,14 @@ def main():  # noqa: C901
 
     export_and_lower(model, config, args)
 
+    if args.backend == "cuda" and torch.cuda.is_available():
+        peak_alloc_gb = torch.cuda.max_memory_allocated() / 1e9
+        peak_reserved_gb = torch.cuda.max_memory_reserved() / 1e9
+        print(
+            f"[CUDA peak memory] allocated={peak_alloc_gb:.2f} GB, "
+            f"reserved={peak_reserved_gb:.2f} GB"
+        )
+
 
 if __name__ == "__main__":
     main()