[DOC] Add Kubernetes deployment guide with CPUs (#14865)

docs/source/conf.py

@@ -85,6 +85,7 @@ html_static_path = ["_static"]
 html_js_files = ["custom.js"]
 html_css_files = ["custom.css"]
 
+myst_heading_anchors = 2
 myst_url_schemes = {
     'http': None,
     'https': None,

docs/source/deployment/k8s.md

@@ -4,6 +4,9 @@
 
 Deploying vLLM on Kubernetes is a scalable and efficient way to serve machine learning models. This guide walks you through deploying vLLM using native Kubernetes.
 
+* [Deployment with CPUs](#deployment-with-cpus)
+* [Deployment with GPUs](#deployment-with-gpus)
+
 Alternatively, you can deploy vLLM to Kubernetes using any of the following:
 * [Helm](frameworks/helm.md)
 * [InftyAI/llmaz](integrations/llmaz.md)
@@ -14,11 +17,107 @@ Alternatively, you can deploy vLLM to Kubernetes using any of the following:
 * [vllm-project/aibrix](https://github.com/vllm-project/aibrix)
 * [vllm-project/production-stack](integrations/production-stack.md)
 
-## Pre-requisite
+## Deployment with CPUs
+
+:::{note}
+The use of CPUs here is for demonstration and testing purposes only and its performance will not be on par with GPUs.
+:::
+
+First, create a Kubernetes PVC and Secret for downloading and storing Hugging Face model:
+
+```bash
+cat <<EOF |kubectl apply -f -
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: vllm-models
+spec:
+  accessModes:
+    - ReadWriteOnce
+  volumeMode: Filesystem
+  resources:
+    requests:
+      storage: 50Gi
+---
+apiVersion: v1
+kind: Secret
+metadata:
+  name: hf-token-secret
+type: Opaque
+data:
+  token: $(HF_TOKEN)
+```
+
+Next, start the vLLM server as a Kubernetes Deployment and Service:
+
+```bash
+cat <<EOF |kubectl apply -f -
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: vllm-server
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app.kubernetes.io/name: vllm
+  template:
+    metadata:
+      labels:
+        app.kubernetes.io/name: vllm
+    spec:
+      containers:
+      - name: vllm
+        image: vllm/vllm-openai:latest
+        command: ["/bin/sh", "-c"]
+        args: [
+          "vllm serve meta-llama/Llama-3.2-1B-Instruct"
+        ]
+        env:
+        - name: HUGGING_FACE_HUB_TOKEN
+          valueFrom:
+            secretKeyRef:
+              name: hf-token-secret
+              key: token
+        ports:
+          - containerPort: 8000
+        volumeMounts:
+          - name: llama-storage
+            mountPath: /root/.cache/huggingface
+      volumes:
+      - name: llama-storage
+        persistentVolumeClaim:
+          claimName: vllm-models
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: vllm-server
+spec:
+  selector:
+    app.kubernetes.io/name: vllm
+  ports:
+  - protocol: TCP
+    port: 8000
+    targetPort: 8000
+  type: ClusterIP
+EOF
+```
+
+We can verify that the vLLM server has started successfully via the logs (this might take a couple of minutes to download the model):
+
+```console
+kubectl logs -l app.kubernetes.io/name=vllm
+...
+INFO:     Started server process [1]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
+```
 
-Ensure that you have a running [Kubernetes cluster with GPUs](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/).
+## Deployment with GPUs
 
-## Deployment using native K8s
+**Pre-requisite**: Ensure that you have a running [Kubernetes cluster with GPUs](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/).
 
 1. Create a PVC, Secret and Deployment for vLLM