Kubernetes LLM Labs
The labs turn the guide into operator exercises. They are not a hosted sandbox. They are field runbooks you can run in your own cluster, adapt to a platform review, or use as interview-grade design drills.
Each lab has the same contract: objective, prerequisites, hands-on tasks, validation signals, failure drills, and links back to the deeper guide.
01vLLM inference labDeploy a GPU-backed OpenAI-compatible endpoint and validate token latency.02RAG retrieval labOperate ingestion, vector search, retrieval quality, and answer evaluation.03Production readiness labCheck security, rollback, quota, cost, and observability before launch.04Observability labBuild the signals that make LLM incidents debuggable.
Lab prerequisites
| Requirement | Why it matters |
|---|---|
| Kubernetes cluster access | You need namespace, workload, service, and log visibility. |
| GPU node pool for inference labs | LLM serving exercises depend on accelerator scheduling and runtime health. |
| Metrics stack | Prometheus, Grafana, OpenTelemetry, or equivalent telemetry makes validation concrete. |
| GitOps or manifest workflow | Labs are easier to review when every change is versioned. |
| Test prompts and evaluation cases | LLM workloads need behavior checks, not only pod readiness. |
How to use the labs
- Read the related architecture guide before applying manifests.
- Run the baseline task without optimization.
- Capture metrics before changing runtime flags or autoscaling policy.
- Trigger one failure drill.
- Write down which signal detected the failure first.
New labs should follow the Content Review Checklist: objective, prerequisites, manifests or commands, validation signals, failure drills, and expected signals.
Recommended path
Start with K8s LLM: Kubernetes LLM Platform Guide, continue to GPU Node Pool Kubernetes, run the vLLM inference lab, then move to RAG On Kubernetes and the RAG retrieval lab.