diff --git a/ray_serve/serve_llm.py b/ray_serve/serve_llm.py
index 1992083..9b44b0c 100644
--- a/ray_serve/serve_llm.py
+++ b/ray_serve/serve_llm.py
@@ -83,7 +83,15 @@ class LLMDeployment:
         engine_args = AsyncEngineArgs(**engine_kwargs)
         self.engine = AsyncLLMEngine.from_engine_args(engine_args)
         self.SamplingParams = SamplingParams
+        self.default_max_tokens = int(os.environ.get("DEFAULT_MAX_TOKENS", "256"))
+        # Llama 3 stop tokens — safety net in case V1 engine ignores max_tokens
+        self._stop_token_ids = [
+            128001,  # <|end_of_text|>
+            128009,  # <|eot_id|>
+        ]
         print(f"Model {self.model_id} async engine created")
+        print(f"Default max tokens: {self.default_max_tokens}")
+        print(f"Stop token IDs: {self._stop_token_ids}")
 
         # MLflow metrics — import locally to avoid cloudpickle
         # serializing a module reference that fails on the worker
@@ -128,7 +136,10 @@ class LLMDeployment:
         """
         messages = request.get("messages", [])
         temperature = request.get("temperature", 0.7)
-        max_tokens = request.get("max_tokens", 256)
+        max_tokens = min(
+            request.get("max_tokens", self.default_max_tokens),
+            self.max_model_len,
+        )
         top_p = request.get("top_p", 1.0)
         stop = request.get("stop")
 
@@ -140,6 +151,7 @@ class LLMDeployment:
             max_tokens=max_tokens,
             top_p=top_p,
             stop=stop,
+            stop_token_ids=self._stop_token_ids,
         )
 
         start_time = time.time()