diff --git a/examples/distributed/bang_launch.py b/examples/distributed/bang_launch.py
index 518935b5..18f366e6 100644
--- a/examples/distributed/bang_launch.py
+++ b/examples/distributed/bang_launch.py
@@ -30,6 +30,12 @@ def parse_args():
         action="store_true",
         help="whether to generate the standard results.",
     )
+    parser.add_argument(
+        "--run_torch",
+        default=False,
+        action="store_true",
+        help="whether to run model using PyTorch.",
+    )
     args = parser.parse_args()
     print("arg setting: ", args)
     return (
@@ -40,6 +46,7 @@ def parse_args():
         args.batch_size,
         args.length,
         args.gen_std,
+        args.run_torch
     )
 
 
@@ -153,12 +160,49 @@ def getDiff(base, test):
           f"Max relative difference: {max_relative_diff}")
     return max_absolute_diff, max_relative_diff
 
+def run_pytorch(model, n=10):
+    from onnxsim import simplify
+    from onnx import version_converter
+    from onnx2torch import convert
+    import torch
+    import torch_mlu
+
+    model, check = simplify(model)
+    target_version = 16
+    converted_model = version_converter.convert_version(model, target_version)
+    device = torch.device("mlu")
+    torch_model = convert(converted_model)
+    torch_model.to(device)
+    torch_model.eval()
+    print("Run model using pytorch.")
+
+    runtime = backend.BangRuntime(0)
+    stub = OnnxStub(model, runtime)
+    inputs = []
+    for i, (name, tensor) in enumerate(stub.inputs.items()):
+        input = torch.from_numpy(np.load(f"./data/input_{i}.npy"))
+        input.to(device)
+        inputs.append(input)
+
+    torch.mlu.synchronize()
+    begin = time.time()
+    with torch.no_grad():
+        for _ in range(n):
+            outputs = torch_model(*inputs)
+            torch.mlu.synchronize()
+    end = time.time()
+    
+    avg_time = (end - begin) / n
+    print(f"average time: {avg_time}")
+    return outputs
+
 
 def main():
-    nnodes, nproc_per_node, name, model_path, bs, length, gen_std = parse_args()
+    nnodes, nproc_per_node, name, model_path, bs, length, gen_std, run_torch = parse_args()
 
     model = onnx.load(model_path)
 
+
     # generate standart output
     if gen_std:
         print("Generate inputs and outputs.")
@@ -190,6 +234,9 @@ def main():
 
     for w in workers:
         w.join()
+        
+    if run_torch:
+        run_pytorch(model)
 
 
 if __name__ == "__main__":