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 3, 20:15 UTC
[Doc] Fix typo in llm-d documentation link (#42397)
Signed-off-by: Florian Woerner <florian.woerner@onmyown.io> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Florian Woerner committed
May 12, 2026 at 13:26 UTC
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docs/deployment/integrations/llm-d.md
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vLLM can be deployed with [llm-d](https://github.com/llm-d/llm-d), a Kubernetes-native distributed inference serving stack providing well-lit paths for anyone to serve large generative AI models at scale. It helps achieve the fastest "time to state-of-the-art (SOTA) performance" for key OSS models across most hardware accelerators and infrastructure providers.
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-You can use vLLM with llm-d directly by following [this guide](https://llm-d.ai/docs/guide) or via [KServe's LLMInferenceService](https://kserve.github.io/website/docs/model-serving/generative-inference/llmisvc/llmisvc-overview).
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+You can use vLLM with llm-d directly by following [the official guides](https://llm-d.ai/docs/guides) or via [KServe's LLMInferenceService](https://kserve.github.io/website/docs/model-serving/generative-inference/llmisvc/llmisvc-overview).