chore: import upstream snapshot with attribution

python/ray/llm/examples/sglang/serve_sglang_multinode_example.py

@@ -0,0 +1,46 @@
+"""Multi-node SGLang serving example with tensor and pipeline parallelism.
+
+Requirements:
+    - 2 nodes with 4 GPUs each (8 GPUs total for tp_size=4, pp_size=2)
+    - pip install ray[serve,llm] "sglang[all,ray]"
+    - Set RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=0
+
+Usage:
+    RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=0 serve run serve_sglang_multinode_example:app
+"""
+
+from ray import serve
+from ray.llm._internal.serve.engines.sglang import SGLangServer
+from ray.serve.llm import LLMConfig, build_openai_app
+
+llm_config = LLMConfig(
+    model_loading_config={
+        "model_id": "Llama-3.1-70B-Instruct",
+        "model_source": "meta-llama/Llama-3.1-70B-Instruct",
+    },
+    deployment_config={
+        "autoscaling_config": {
+            "min_replicas": 1,
+            "max_replicas": 2,
+            "target_ongoing_requests": 4,
+        }
+    },
+    # PACK fills GPUs on each node before moving to the next.
+    # With 8 bundles across 2 nodes (4 GPUs each), each node gets 4 bundles.
+    placement_group_config={
+        "placement_group_bundles": [{"GPU": 1}] * 8,
+        "placement_group_strategy": "PACK",
+    },
+    server_cls=SGLangServer,
+    engine_kwargs={
+        "model_path": "meta-llama/Llama-3.1-70B-Instruct",
+        "tp_size": 4,
+        "pp_size": 2,
+        "mem_fraction_static": 0.8,
+    },
+)
+
+app = build_openai_app({"llm_configs": [llm_config]})
+
+if __name__ == "__main__":
+    serve.run(app, blocking=True)