diff --git a/examples/inference/distributed/llava_next_video.py b/examples/inference/distributed/llava_next_video.py
index 4c18e65843b..50dc81face6 100644
--- a/examples/inference/distributed/llava_next_video.py
+++ b/examples/inference/distributed/llava_next_video.py
@@ -22,7 +22,9 @@
 import av
 from huggingface_hub import hf_hub_download
 import json
-from accelerate.utils import gather_object
+import queue
+from concurrent.futures import ThreadPoolExecutor
+import pathlib
 
 START_TIME = time.strftime("%Y%m%d_%H%M%S")
 DTYPE_MAP = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}
@@ -31,7 +33,7 @@
 """
 Example:
 
-accelerate launch --num_processes=2 llava_next_video.py 
+accelerate launch llava_next_video.py 
 """
 
 
@@ -39,6 +41,7 @@ def main(
     model_name: str = "llava-hf/LLaVA-NeXT-Video-7B-hf",
     save_dir: str = "./evaluation/examples",
     dtype: str = "fp16",
+    num_workers: int = 1,
     low_mem: bool = True,
 ):
     # Start up the distributed environment without needing the Accelerator.
@@ -46,7 +49,7 @@ def main(
 
     processor = LlavaNextVideoProcessor.from_pretrained(model_name)
     model = LlavaNextVideoForConditionalGeneration.from_pretrained(
-        model_name, torch_dtype=DTYPE_MAP[dtype], low_cpu_mem_usage=low_mem, device_map=distributed_state.device
+        model_name, torch_dtype=dtype[DTYPE_MAP], low_cpu_mem_usage=low_mem, device_map=distributed_state.device
     )
 
     if distributed_state.is_main_process:
@@ -109,33 +112,43 @@ def main(
         for i in range(0, len(conversations))
     ]
 
-    count = 0
-    distributed_state.num_processes = len(formatted_prompts)
-    with distributed_state.split_between_processes(formatted_prompts) as prompts:
-        input = processor(text=prompts, videos=video, return_tensors="pt").to(model.device)
-        output = model.generate(**input, max_new_tokens=60)
-        generated_text = processor.decode(output[0][2:], skip_special_tokens=True)
-
-    distributed_state.wait_for_everyone()
-
-    answers = gather_object(generated_text)
-    input_prompts = gather_object(prompts)
-
-    if distributed_state.is_main_process:
-        for ans, prompt in zip(answers, input_prompts):
-            count += 1
-            example_file = f"example_{count}"
-            temp_dir = os.path.join(save_dir, example_file)
-
-            metadata = {
-                "prompt": prompt,
-                "generated_answer": ans,
-            }
-            with open(temp_dir, "w") as f:
-                json.dump(metadata, f, indent=4)
+    def save_results(output_queue: queue.Queue, output_dir: pathlib.Path):
+        count = 0
+        while True:
+            try:
+                item = output_queue.get(timeout=5)
+                if item is None:
+                    break
+                example_file = f"example_{count}"
+                temp_dir = os.path.join(output_dir, example_file)
+
+                metadata = {
+                    "prompt": item[0],
+                    "generated_answer": item[1],
+                }
+                with open(temp_dir, "w") as f:
+                    json.dump(metadata, f, indent=4)
+                count += 1
+
+            except queue.Empty:
+                continue
 
-    if distributed_state.is_main_process:
-        print(f">>> Video answer generation Finished. Saved in {save_dir}")
+    distributed_state.num_processes = len(formatted_prompts)
+    output_queue = queue.Queue()
+    save_thread = ThreadPoolExecutor(max_workers=num_workers)
+    save_future = save_thread.submit(save_results, output_queue, save_dir)
+
+    try:
+        with distributed_state.split_between_processes(formatted_prompts) as prompt:
+            input = processor(text=prompt, videos=video, padding=True, return_tensors="pt").to(model.device)
+            output = model.generate(**input, max_new_tokens=60)
+            generated_text = processor.decode(output[0][2:], skip_special_tokens=True)
+            output_queue.put((prompt, generated_text))
+    finally:
+        output_queue.put(None)
+        save_thread.shutdown(wait=True)
+
+    save_future.result()
 
 
 def read_video_pyav(container, indices):