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Documentation Index

Fetch the complete documentation index at: https://docs.coreweave.com/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

Now that your infrastructure and monitoring are set up, you’ll deploy the vLLM inference service. This step covers configuring your deployment, deploying the service, and verifying it’s working correctly.

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Step 1: Configure your deployment

Choose from the example configurations in the hack/ directory, or customize values.yaml for your specific model and requirements. For this guide, we’ll deploy Llama 3.1 8B Instruct. Navigate to the inference/basic directory, and run the following command: 
cp hack/values-llama-small.yaml my-values.yaml

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Step 2: Update cluster-specific settings

Edit my-values.yaml and update the following required fields: 
ingress:
  clusterName: "your-cluster-name"    # Replace with your CKS cluster name
  orgID: "your-org-id"                # Replace with your organization ID

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Step 3: Deploy the vLLM service

Install the vLLM inference chart: 
helm install basic-inference ./ \
  --namespace inference \
  --create-namespace \
  -f my-values.yaml
You should see output similar to the following: 
NAME: basic-inference
LAST DEPLOYED: Mon Aug 18:10:27
NAMESPACE: inference
STATUS: deployed
REVISION: 1
TEST SUITE: None

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Step 4: Monitor deployment progress

Watch the deployment status by running the following command to check pod status: 
kubectl get pods -n inference -w
The initial deployment may take several minutes as the model weights are downloaded and cached. You should see output similar to: 
NAME                               READY   STATUS    RESTARTS   AGE
basic-inference-577c5675c8-vm2nc   1/1     Running   0          13m
Once you see the pod is running, exit the process and go to the next step.
Debugging tipModel download failures:
  • Ensure internet connectivity from worker nodes
  • Check Hugging Face token for gated models
  • Verify sufficient storage in model cache PVC
Pods stuck in pending state:
  • Check GPU node availability: kubectl get nodes -l node-role.kubernetes.io/worker=true
  • Verify resource requests don’t exceed node capacity

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Step 5: Check service and ingress

Verify that the service and ingress are properly configured: 
kubectl get svc,ingress -n inference
You should see output similar to: 
NAME                         TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE
service/basic-inference-vllm ClusterIP   10.96.123.45    <none>        8000/TCP  5m

NAME                                      CLASS    HOSTS                                         ADDRESS      PORTS     AGE
ingress.networking.k8s.io/basic-inference traefik  basic-inference.your-cluster.coreweave.cloud  ***.**       80, 443   5m
Debugging tipIngress not accessible:
  • Confirm Traefik is running: kubectl get pods -n traefik
  • Check cert-manager for certificate issues: kubectl get certificates -n inference
  • Verify DNS resolution to your cluster’s load balancer

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Step 6: Access and test your inference service

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Get the service endpoint

Retrieve the external URL for your vLLM service: 
export VLLM_ENDPOINT="$(kubectl get ingress basic-inference -n inference -o=jsonpath='{.spec.rules[0].host}')"
echo "Your vLLM endpoint: https://$VLLM_ENDPOINT"

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Test service health

Verify the service is responding: 
curl -s -o /dev/null -w "%{http_code}" https://$VLLM_ENDPOINT/health
You should see 200.

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Get available models

List the loaded models: 
export VLLM_MODEL="$(curl -s https://$VLLM_ENDPOINT/v1/models | jq -r '.data[].id')"
echo "Available model: $VLLM_MODEL"
You should see the following output: 
Available model: meta-llama/Llama-3.1-8B-Instruct

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Run inference

Test the model with a simple chat completion: 
curl -X POST "https://$VLLM_ENDPOINT/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "'"$VLLM_MODEL"'",
        "messages": [
          { "role": "system", "content": "You are a helpful AI assistant." },
          { "role": "user", "content": "Explain the benefits of GPU acceleration for LLM inference." }
        ],
        "max_tokens": 500,
        "temperature": 0.7
      }'
You should see the returned output in JSON.

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What’s next

Your vLLM inference service is now deployed and running! In the next step, you’ll monitor performance and test autoscaling. The initial model download can take 10-30 minutes depending on the model size and network conditions. You can monitor the download progress in the pod logs: 
kubectl logs -n inference -l app=basic-inference -f
General debugging commands for this guide: 
# Check vLLM pod logs
kubectl logs -n inference -l app=basic-inference

# Describe pod for events
kubectl describe pod -n inference -l app=basic-inference

# Check ingress status
kubectl describe ingress basic-inference -n inference

# Monitor resource usage
kubectl top pods -n inference
Last modified on April 20, 2026