A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 9, 10:00 UTC
[Doc] Update outdated link to Ray documentation (#32660)
Signed-off-by: graftim <38649219+graftim@users.noreply.github.com>
graftim committed
Jan 29, 2026 at 09:56 UTC
d697581a7c28668d00b2284477e20a2cd774ea6e
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docs/serving/openai_compatible_server.md
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@@ -1067,4 +1067,4 @@ Key capabilities:
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The following example shows how to deploy a large model like DeepSeek R1 with Ray Serve LLM: [examples/online_serving/ray_serve_deepseek.py](../../examples/online_serving/ray_serve_deepseek.py).
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-Learn more about Ray Serve LLM with the official [Ray Serve LLM documentation](https://docs.ray.io/en/latest/serve/llm/serving-llms.html).
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+Learn more about Ray Serve LLM with the official [Ray Serve LLM documentation](https://docs.ray.io/en/latest/serve/llm/index.html).