diff --git a/benchmarks/benchmark_latency.py b/benchmarks/benchmark_latency.py
index 2fdc08c5..0f223571 100644
--- a/benchmarks/benchmark_latency.py
+++ b/benchmarks/benchmark_latency.py
@@ -16,18 +16,17 @@ def main(args: argparse.Namespace):
 
     # NOTE(woosuk): If the request cannot be processed in a single batch,
     # the engine will automatically process the request in multiple batches.
-    llm = LLM(
-        model=args.model,
-        tokenizer=args.tokenizer,
-        quantization=args.quantization,
-        tensor_parallel_size=args.tensor_parallel_size,
-        trust_remote_code=args.trust_remote_code,
-        dtype=args.dtype,
-        enforce_eager=args.enforce_eager,
-        kv_cache_dtype=args.kv_cache_dtype,
-        device=args.device,
-        ray_workers_use_nsight=args.ray_workers_use_nsight,
-    )
+    llm = LLM(model=args.model,
+              tokenizer=args.tokenizer,
+              quantization=args.quantization,
+              tensor_parallel_size=args.tensor_parallel_size,
+              trust_remote_code=args.trust_remote_code,
+              dtype=args.dtype,
+              enforce_eager=args.enforce_eager,
+              kv_cache_dtype=args.kv_cache_dtype,
+              device=args.device,
+              ray_workers_use_nsight=args.ray_workers_use_nsight,
+              download_dir=args.download_dir)
 
     sampling_params = SamplingParams(
         n=args.n,
@@ -151,5 +150,10 @@ if __name__ == '__main__':
         action='store_true',
         help="If specified, use nsight to profile ray workers",
     )
+    parser.add_argument('--download-dir',
+                        type=str,
+                        default=None,
+                        help='directory to download and load the weights, '
+                        'default to the default cache dir of huggingface')
     args = parser.parse_args()
     main(args)
diff --git a/benchmarks/benchmark_throughput.py b/benchmarks/benchmark_throughput.py
index fae4776b..6ccdd865 100644
--- a/benchmarks/benchmark_throughput.py
+++ b/benchmarks/benchmark_throughput.py
@@ -75,6 +75,7 @@ def run_vllm(
     device: str,
     enable_prefix_caching: bool,
     gpu_memory_utilization: float = 0.9,
+    download_dir: Optional[str] = None,
 ) -> float:
     from vllm import LLM, SamplingParams
     llm = LLM(model=model,
@@ -89,7 +90,8 @@ def run_vllm(
               enforce_eager=enforce_eager,
               kv_cache_dtype=kv_cache_dtype,
               device=device,
-              enable_prefix_caching=enable_prefix_caching)
+              enable_prefix_caching=enable_prefix_caching,
+              download_dir=download_dir)
 
     # Add the requests to the engine.
     for prompt, _, output_len in requests:
@@ -208,12 +210,14 @@ def main(args: argparse.Namespace):
                                    args.output_len)
 
     if args.backend == "vllm":
-        elapsed_time = run_vllm(
-            requests, args.model, args.tokenizer, args.quantization,
-            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)
+        elapsed_time = run_vllm(requests, args.model, args.tokenizer,
+                                args.quantization, 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.download_dir)
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
         elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
@@ -314,6 +318,11 @@ if __name__ == "__main__":
         "--enable-prefix-caching",
         action='store_true',
         help="enable automatic prefix caching for vLLM backend.")
+    parser.add_argument('--download-dir',
+                        type=str,
+                        default=None,
+                        help='directory to download and load the weights, '
+                        'default to the default cache dir of huggingface')
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model