diff --git a/llms/mlx_lm/chat.py b/llms/mlx_lm/chat.py
index 85d32d5f..2641b023 100644
--- a/llms/mlx_lm/chat.py
+++ b/llms/mlx_lm/chat.py
@@ -15,6 +15,25 @@
 DEFAULT_MODEL = "mlx-community/Llama-3.2-3B-Instruct-4bit"
 
 
+def share_message(world, prompt):
+    if world.size() == 1:
+        return
+
+    if world.rank() == 0:
+        prompt_array = mx.array(prompt.encode())
+        for i in range(1, world.size()):
+            mx.eval(mx.send(prompt_array, i))
+        world.barrier()
+
+    else:
+        prompt_array = mx.recv(0)
+        mx.eval(prompt_array)
+        prompt = bytes(prompt_array).decode()
+        world.barrier()
+
+    return prompt
+
+
 def setup_arg_parser():
     """Set up and return the argument parser."""
     parser = argparse.ArgumentParser(description="Chat with an LLM")
@@ -53,6 +72,7 @@ def setup_arg_parser():
 
 
 def main():
+    world = mx.distributed.init()
     parser = setup_arg_parser()
     args = parser.parse_args()
 
@@ -62,18 +82,23 @@ def main():
         args.model,
         adapter_path=args.adapter_path,
         tokenizer_config={"trust_remote_code": True},
+        sequential_load=mx.distributed.init().size() > 1,
     )
 
+    print(f"Node {world.rank()} of {world.size()}", flush=True)
     print(f"[INFO] Starting chat session with {args.model}. To exit, enter 'q'.")
     prompt_cache = make_prompt_cache(model, args.max_kv_size)
     while True:
-        query = input(">> ")
-        if query == "q":
-            break
-        messages = [{"role": "user", "content": query}]
-        prompt = tokenizer.apply_chat_template(
-            messages, tokenize=False, add_generation_prompt=True
-        )
+        prompt = None
+        if world.rank() == 0:
+            query = input(">> ")
+            if query == "q":
+                break
+            messages = [{"role": "user", "content": query}]
+            prompt = tokenizer.apply_chat_template(
+                messages, tokenize=False, add_generation_prompt=True
+            )
+        prompt = share_message(world, prompt)
         for response in stream_generate(
             model,
             tokenizer,
@@ -83,9 +108,12 @@ def main():
             top_p=args.top_p,
             prompt_cache=prompt_cache,
         ):
-            print(response, flush=True, end="")
-        print()
+            if world.rank() == 0:
+                print(response, flush=True, end="")
+        if world.rank() == 0:
+            print()
 
 
 if __name__ == "__main__":
     main()
+