diff --git a/docker/llm/serving/xpu/docker/benchmark_vllm_throughput.py b/docker/llm/serving/xpu/docker/benchmark_vllm_throughput.py
index 01cdb461..6defa576 100644
--- a/docker/llm/serving/xpu/docker/benchmark_vllm_throughput.py
+++ b/docker/llm/serving/xpu/docker/benchmark_vllm_throughput.py
@@ -76,6 +76,7 @@ def run_vllm(
     enable_prefix_caching: bool,
     gpu_memory_utilization: float = 0.9,
     load_in_low_bit: str = "sym_int4",
+    max_num_batched_tokens: int = 5000,
 ) -> float:
     from vllm import SamplingParams
     from ipex_llm.vllm.xpu.engine import IPEXLLMClass as LLM
@@ -92,9 +93,30 @@ def run_vllm(
               kv_cache_dtype=kv_cache_dtype,
               device=device,
               enable_prefix_caching=enable_prefix_caching,
-              load_in_low_bit=load_in_low_bit)
+              load_in_low_bit=load_in_low_bit,
+              max_num_batched_tokens=max_num_batched_tokens,)
+
 
     # Add the requests to the engine.
+    warm_prompt = "hi " * (1024 - 1)
+    warm_requests = [(warm_prompt, 1024, 1024)
+                    for _ in range(8)]
+    for prompt, _, output_len in warm_requests:
+        sampling_params = SamplingParams(
+            n=n,
+            temperature=0.0 if use_beam_search else 1.0,
+            top_p=1.0,
+            use_beam_search=use_beam_search,
+            ignore_eos=True,
+            max_tokens=output_len,
+        )
+        llm._add_request(
+            prompt=prompt,
+            prompt_token_ids=None,
+            sampling_params=sampling_params,
+        )
+    llm._run_engine(use_tqdm=True)
+
     for prompt, _, output_len in requests:
         sampling_params = SamplingParams(
             n=n,
@@ -216,7 +238,7 @@ def main(args: argparse.Namespace):
             args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
             args.trust_remote_code, args.dtype, args.max_model_len,
             args.enforce_eager, args.kv_cache_dtype, args.device,
-            args.enable_prefix_caching, args.gpu_memory_utilization, args.load_in_low_bit)
+            args.enable_prefix_caching, args.gpu_memory_utilization, args.load_in_low_bit, args.max_num_batched_tokens)
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
         elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
@@ -323,6 +345,12 @@ if __name__ == "__main__":
         choices=["sym_int4", "fp8", "fp16"],
         default="sym_int4",
         help="Low-bit format quantization with IPEX-LLM")
+
+    parser.add_argument('--max-num-batched-tokens',
+                        type=int,
+                        default=5000,
+                        help='maximum number of batched tokens per iteration')
+
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model
@@ -354,4 +382,5 @@ if __name__ == "__main__":
         if args.tokenizer != args.model:
             raise ValueError("Tokenizer must be the same as the model for MII "
                              "backend.")
-    main(args)
\ No newline at end of file
+    main(args)
+