Kubernetes LLM guided labs

Practice the platform checks behind production LLM systems.

K8sLLM Labs turns Kubernetes LLM architecture into interactive operator challenges: type commands in a lab terminal, inspect Kubernetes output, unlock hints, validate readiness, and keep private progress on this device.

Browse challengesFollow roadmapRead docs
Pull architecture guide->Choose challenge
Type kubectl or curl->Inspect terminal output
Regex check passes->Step completed
Open hint if blocked->Reveal solution if needed
Example checkpaste_regex
kubectl get nodes -L accelerator,nvidia.com/gpu.product

The check passes when the output proves GPU placement, node labeling, or accelerator scheduling evidence.

Challenge of the Week

vLLM Inference Challenge

Deploy a GPU-backed OpenAI-compatible endpoint and prove scheduling, health, TTFT, queueing, and rollback readiness.

Hard75 minModel servingAI infrastructure engineer
Start challengeRead guideFollow roadmap
Free challenges12
Learning paths5
Guided checks68
Progressdevice

Product paths

Built for platform engineers, DevOps, MLOps, and AI infrastructure learners.

Kubernetes LLM Foundations

Build the cluster mental model needed before serving models.

vLLM Production Serving

Move from runtime deployment to latency, health, and rollout checks.

RAG Platform Engineering

Operate ingestion, retrieval, policy, answer quality, and evaluation.

LLM Observability and Cost

Connect user latency, runtime saturation, GPU pressure, and economics.

Production Readiness for AI Workloads

Review security, rollout, tenancy, rollback, and platform ownership.

Challenge catalog

Operator lab index

6/6 visible
Topic
Difficulty
Path
01
Model servingvllm-production-serving / production-readiness-ai

vLLM Inference Challenge

Deploy a GPU-backed OpenAI-compatible endpoint and prove scheduling, health, TTFT, queueing, and rollback readiness.

DifficultyHard
Time75 min
Progress statusnot started
Open labDocs
02
RAGrag-platform-engineering

RAG Retrieval Challenge

Operate ingestion, metadata filters, vector retrieval, answer evaluation, and failure drills for production RAG.

DifficultyMedium
Time60 min
Progress statusnot started
Open labDocs
03
Productionproduction-readiness-ai / kubernetes-llm-foundations

Production Readiness Challenge

Run a launch review across security, quota, rollout, observability, cost, and ownership before live traffic.

DifficultyHard
Time50 min
Progress statusnot started
Open labDocs
04
Observabilityllm-observability-cost / production-readiness-ai

LLM Observability Challenge

Build the signal model needed to debug user latency, runtime saturation, GPU pressure, traces, logs, and alerts.

DifficultyMedium
Time45 min
Progress statusnot started
Open labDocs
05
Model servingvllm-production-serving

vLLM Kubernetes Deployment Lab

Design the deployment contract for vLLM with model cache, readiness, runtime flags, and service exposure.

DifficultyMedium
Time55 min
Progress statusnot started
Open labDocs
06
Architecturekubernetes-llm-foundations / vllm-production-serving

KServe vs Ray Serve Decision Lab

Choose the serving abstraction by ownership model, CRDs, graph complexity, autoscaling, and rollout needs.

DifficultyMedium
Time35 min
Progress statusnot started
Open labDocs

Advanced lab access

Get notified when downloadable kits, deeper scenarios, and review worksheets are ready.

For now this saves interest on this device. A real signup form will include consent and email delivery before launch.