add kuberay integration

Signed-off-by: Ricardo Decal <[email protected]>

Author: crypdick

docs/deployment/integrations/kuberay.md

@@ -0,0 +1,61 @@
+---
+title: KubeRay
+---
+[](){ #deployment-kuberay }
+
+[KubeRay](https://github.com/ray-project/kuberay) provides a Kubernetes-native way to run vLLM workloads on Ray clusters.
+A Ray cluster can be declared in YAML, and the operator then handles pod scheduling, networking configuration, restarts, and rolling upgrades—all while preserving the familiar Kubernetes experience.
+
+---
+
+## Why KubeRay instead of manual scripts?
+
+| Feature | Manual scripts | KubeRay |
+|---------|-----------------------------------------------------------|---------|
+| Cluster bootstrap | Manually SSH into every node and run a script | One command to create or update the whole cluster: `kubectl apply -f cluster.yaml` |
+| Fault-tolerance | Nodes must be restarted by hand | Pods are automatically rescheduled; head-node fail-over supported |
+| Autoscaling | Unsupported | Native horizontal **and** vertical autoscaling via Ray Autoscaler & Kubernetes HPA |
+| Upgrades | Tear down & re-create manually | Rolling updates handled by the operator |
+| Monitoring | ad-hoc | Distributed observability with Ray Dashboard |
+| Declarative config | Bash flags & environment variables | Git-ops-friendly YAML CRDs (RayCluster/RayService) |
+
+Using KubeRay reduces the operational burden and simplifies integration of Ray + vLLM with existing Kubernetes workflows (CI/CD, secrets, storage classes, etc.).
+
+---
+
+## Quick start
+
+1. Install the KubeRay operator (via Helm or `kubectl apply`).
+2. Create a `RayService` that runs vLLM.
+
+```bash
+# FIXME create this yaml before merging PR
+kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/refs/heads/master/ray-operator/config/samples/vllm/ray-service.vllm.yaml
+```
+
+The YAML above spins up a Ray cluster and a Ray Serve application that serves the
+`meta-llama/Meta-Llama-3-8B-Instruct` model using vLLM. Wait until the
+`RayService` reports **RUNNING**, then port-forward and query the model:
+
+```bash
+kubectl port-forward svc/llama-3-8b-serve-svc 8000 &
+
+curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+        "model": "meta-llama/Meta-Llama-3-8B-Instruct",
+        "messages": [
+          {"role": "system", "content": "You are a helpful assistant."},
+          {"role": "user", "content": "Provide a brief sentence describing the Ray open-source project."}
+        ],
+        "temperature": 0.7
+      }'
+```
+
+---
+
+## Learn more
+
+* ["Serve a Large Language Model with vLLM on Kubernetes"](https://docs.ray.io/en/latest/cluster/kubernetes/examples/vllm-rayservice.html):
+  End-to-end walkthrough for deploying Llama-3 8B with `RayService`.
+* [KubeRay documentation](https://docs.ray.io/en/latest/cluster/kubernetes/index.html)