Use vLLM on GKE to run inference with Llama 4

Access Llama 4 by using Hugging Face

To use Hugging Face to access Llama 4, do the following:

1. Sign the consent agreement to use Llama 4.
2. Create a Hugging Face read access token.
3. Copy and save the read access token value. You use it later in this tutorial.

Prepare your environment

To prepare your environment, set the following variables:

gcloud config set project PROJECT_ID gcloud config set billing/quota_project PROJECT_ID export PROJECT_ID=$(gcloud config get project) export RESERVATION_URL=RESERVATION_URL export REGION=REGION export CLUSTER_NAME=CLUSTER_NAME export HUGGING_FACE_TOKEN=HUGGING_FACE_TOKEN export NETWORK=NETWORK_NAME export SUBNETWORK=SUBNETWORK_NAME 

Replace the following:

• PROJECT_ID: the ID of the Google Cloud project where you want to create the GKE cluster.

• RESERVATION_URL: the URL of the reservation that you want to use to create your GKE cluster. Based on the project in which the reservation exists, specify one of the following values:

  • The reservation exists in your project: RESERVATION_NAME

  • The reservation exists in a different project, and your project can use the reservation: projects/RESERVATION_PROJECT_ID/reservations/RESERVATION_NAME

• REGION: the region where you want to create your GKE cluster. You can only create the cluster in the region where you reservation exists.

• CLUSTER_NAME: the name of the GKE cluster to create.

• HUGGING_FACE_TOKEN: the Hugging Face access token that you created in the previous section.

• NETWORK_NAME: the network that the GKE cluster uses. Specify one of the following values:

  • If you created a custom network, then specify the name of your network.

  • Otherwise, specify default.

• SUBNETWORK_NAME: the subnetwork that the GKE cluster uses. Specify one of the following values:

  • If you created a custom subnetwork, then specify the name of your subnetwork. You can only specify a subnetwork that exists in the same region as the reservation.

  • Otherwise, specify default.

Create and configure Google Cloud resources

Follow these instructions in this section to create the required resources.

Create a GKE cluster in Autopilot mode

To create a GKE cluster in Autopilot mode, run the following command:

gcloud container clusters create-auto $CLUSTER_NAME \ --project=$PROJECT_ID \ --region=$REGION \ --release-channel=rapid \ --network=$NETWORK \ --subnetwork=$SUBNETWORK 

The creation of the GKE cluster might take some time to complete. To verify that Google Cloud has finished creating your cluster, go to Kubernetes clusters on the Google Cloud console.

Create a Kubernetes secret to store your Hugging Face credentials

To create a Kubernetes secret to store your Hugging Face credentials, do the following:

1. Configure kubectl to communicate with your GKE cluster:

   gcloud container clusters get-credentials $CLUSTER_NAME \ --location=$REGION 

2. Create a Kubernetes secret that contains the Hugging Face read access token that you created in an earlier step:

   kubectl create secret generic hf-secret \ --from-literal=hf_api_token=${HUGGING_FACE_TOKEN} \ --dry-run=client -o yaml | kubectl apply -f - 

Deploy a vLLM container to your GKE cluster

To deploy the vLLM container to serve the Llama-4-Scout-17B-16E-Instruct model, do the following:

1. Create a vllm-l4-17b.yaml file with your chosen vLLM deployment:

   apiVersion: apps/v1 kind: Deployment metadata:  name: vllm-llama4-deployment spec:  replicas: 1  selector:  matchLabels:  app: llama4-server  template:  metadata:  labels:  app: llama4-server  ai.gke.io/model: llama-4-scout-17b  ai.gke.io/inference-server: vllm  examples.ai.gke.io/source: user-guide  spec:  containers:  - name: inference-server  image: us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20250722_0916_RC01  resources:  requests:  cpu: "10"  memory: "128Gi"  ephemeral-storage: "240Gi"  nvidia.com/gpu: "8"  limits:  cpu: "10"  memory: "128Gi"  ephemeral-storage: "240Gi"  nvidia.com/gpu: "8"  command: ["python3", "-m", "vllm.entrypoints.openai.api_server"]  args:  - --model=$(MODEL_ID)  - --tensor-parallel-size=8  - --host=0.0.0.0  - --port=8000  - --max-model-len=4096  - --max-num-seqs=4  env:  - name: MODEL_ID  value: meta-llama/Llama-4-Scout-17B-16E-Instruct  - name: HUGGING_FACE_HUB_TOKEN  valueFrom:  secretKeyRef:  name: hf-secret  key: hf_api_token  volumeMounts:  - mountPath: /dev/shm  name: dshm  livenessProbe:  httpGet:  path: /health  port: 8000  initialDelaySeconds: 1800  periodSeconds: 10  readinessProbe:  httpGet:  path: /health  port: 8000  initialDelaySeconds: 1800  periodSeconds: 5  volumes:  - name: dshm  emptyDir:  medium: Memory  nodeSelector:  cloud.google.com/gke-accelerator: nvidia-b200  cloud.google.com/reservation-name: RESERVATION_URL  cloud.google.com/reservation-affinity: "specific"  cloud.google.com/gke-gpu-driver-version: latest --- apiVersion: v1 kind: Service metadata:  name: llm-service spec:  selector:  app: llama4-server  type: ClusterIP  ports:  - protocol: TCP  port: 8000  targetPort: 8000 --- apiVersion: monitoring.googleapis.com/v1 kind: PodMonitoring metadata:  name: vllm-llama4-monitoring spec:  selector:  matchLabels:  app: llama4-server  endpoints:  - port: 8000  path: /metrics  interval: 30s 

2. Apply the vllm-l4-17b.yaml file to your GKE cluster:

    kubectl apply -f vllm-l4-17b.yaml 

   During the deployment process, the container must download the Llama-4-Scout-17B-16E-Instruct model from Hugging Face. For this reason, deployment of the container might take up to 30 minutes to complete.

3. To see the completion status, run the following command:

    kubectl wait \ --for=condition=Available \ --timeout=1800s deployment/vllm-llama4-deployment 

   The--timeout=1800s flag allows the command to monitor the deployment for up to 30 minutes.

Interact with Llama 4 by using curl

To verify the Llama 4 Scout model that you deployed, do the following:

1. Set up port forwarding to Llama 4 Scout:

   kubectl port-forward service/llm-service 8000:8000 

2. Open a new terminal window. You can then chat with your model by using curl:

   curl http://127.0.0.1:8000/v1/chat/completions \ -X POST \ -H "Content-Type: application/json" \ -d '{ "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", "messages": [ { "role": "user", "content": "Describe a sailboat in one short sentence?" } ] }' 

3. The output that you see is similar to the following:

   {  "id": "chatcmpl-ec0ad6310c494a889b17600881c06e3d",  "object": "chat.completion",  "created": 1754073279,  "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct",  "choices": [  {  "index": 0,  "message": {  "role": "assistant",  "content": "A sailboat is a type of watercraft that uses the wind for propulsion, typically featuring a hull, mast, and one or more sails.",  "refusal": null,  "annotations": null,  "audio": null,  "function_call": null,  "tool_calls": [],  "reasoning_content": null  },  "logprobs": null,  "finish_reason": "stop",  "stop_reason": null  }  ],  "service_tier": null,  "system_fingerprint": null,  "usage": {  "prompt_tokens": 19,  "total_tokens": 49,  "completion_tokens": 30,  "prompt_tokens_details": null  },  "prompt_logprobs": null,  "kv_transfer_params": null  } 

Observe model performance

To observe the performance of your model, you can use the vLLM dashboard integration in Cloud Monitoring. You can use this dashboard to view critical performance metrics like token throughput, request latency, and error rates.

For information about using Google Cloud Managed Service for Prometheus to collect metrics from your model, see the vLLM observability guidance in the Cloud Monitoring documentation.