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

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

docs/deployment/integrations/kserve.md

@@ -2,4 +2,4 @@
 
 vLLM can be deployed with [KServe](https://github.com/kserve/kserve) on Kubernetes for highly scalable distributed model serving.
 
-Please see [this guide](https://kserve.github.io/website/docs/model-serving/generative-inference/overview) for more details on using vLLM with KServe.
+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

@@ -0,0 +1,5 @@
+# llm-d
+
+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.
+
+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

@@ -12,6 +12,7 @@ Alternatively, you can deploy vLLM to Kubernetes using any of the following:
 
 - [Helm](frameworks/helm.md)
 - [InftyAI/llmaz](integrations/llmaz.md)
+- [llm-d](integrations/llm-d.md)
 - [KAITO](integrations/kaito.md)
 - [KServe](integrations/kserve.md)
 - [Kthena](integrations/kthena.md)