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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 11, 00:00 UTC

docs: Add llm-d integration to the website (#31234)

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>

Yuan Tang committed Dec 23, 2025 at 15:27 UTC 0736f901e7e39d477c8ee00c177f2d67dae48078
3 files changed +7 -1
docs/deployment/integrations/kserve.md
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3 vLLM can be deployed with [KServe](https://github.com/kserve/kserve) on Kubernetes for highly scalable distributed model serving.
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5 -Please see [this guide](https://kserve.github.io/website/docs/model-serving/generative-inference/overview) for more details on using vLLM with KServe.
5 +You can use vLLM with KServe's [Hugging Face serving runtime](https://kserve.github.io/website/docs/model-serving/generative-inference/overview) or via [`LLMInferenceService` that uses llm-d](https://kserve.github.io/website/docs/model-serving/generative-inference/llmisvc/llmisvc-overview).
docs/deployment/integrations/llm-d.md new
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1 +# llm-d
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3 +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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5 +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).
docs/deployment/k8s.md
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@@ -12,6 +12,7 @@ Alternatively, you can deploy vLLM to Kubernetes using any of the following:
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13 - [Helm](frameworks/helm.md)
14 - [InftyAI/llmaz](integrations/llmaz.md)
15 +- [llm-d](integrations/llm-d.md)
16 - [KAITO](integrations/kaito.md)
17 - [KServe](integrations/kserve.md)
18 - [Kthena](integrations/kthena.md)