Pods are healthy, but users still wait for the first token.
Inspect: TTFT, queue wait, tokens/sec
K8sLLM production AI infrastructure guide
Kubernetes + LLM Platform Guide
Learn to design, scale, secure, and operate LLM workloads on Kubernetes. K8sLLM connects cluster primitives to GPU node pools, model serving, RAG, observability, rollout controls, and cost-aware platform decisions.
30+production topics
12interactive challenges
6architecture maps
What are you debugging?
Enter from the symptom, then move into the matching lab.
GPU nodes exist, but inference pods are pending or misplaced.
Inspect: labels, taints, GPU requests
Retrieval is strong, but tenant access is not proven.
Inspect: tenant filters, citations, policy
KServe or Ray Serve is really an ownership decision.
Inspect: contract, graph complexity, rollback
The model works, but launch evidence is incomplete.
Inspect: policy, rollback, cost, latency
Challenge of the Week
vLLM Inference Challenge
Start with the strongest demo: deploy a GPU-backed inference service, inspect terminal evidence, validate placement, measure token latency, and prove the runtime is ready for traffic.
Hard75 minAI infrastructureTTFT and GPU placement
12free guided labs
6production guides
4field notes
0fake customer claims
Choose your path
Start from the production problem you need to solve.
Production scenarios
Read the incidents before they happen.
Field notes turn common Kubernetes LLM failures into practical engineering conversations. Start with symptoms, inspect the right signals, then move into the matching guide or lab.
Browse Field NotesLLM latency on KubernetesPods are healthy, users still wait 14 seconds
green readinessslow first tokenruntime queueing
Platform decision: Separate gateway time, prefill, decode, queue wait, and GPU pressure before adding replicas.
Signals: TTFT, queue wait, tokens/sec, GPU memory, model load timeGPU node pool KubernetesGPU nodes are expensive but underutilized
pending podsidle acceleratorsfragmented profiles
Platform decision: Treat GPU pools as distinct capacity classes with labels, taints, quotas, buffers, and workload lanes.
Signals: pending reasons, GPU utilization, memory profile, queue wait, cost/requestRAG on KubernetesRAG retrieves documents the user should not see
cross-tenant contextwrong citationsmetadata drift
Platform decision: Enforce authorization before context enters the prompt and test retrieval quality as a release gate.
Signals: tenant filters, citation IDs, retrieval recall, unauthorized-document testsKServe vs Ray ServeKServe vs Ray Serve becomes an ownership decision
unclear rollback unitruntime knobs hiddenserving graph sprawl
Platform decision: Pick the serving layer by ownership model, not popularity: platform API or programmable serving graph.
Signals: revision boundary, graph complexity, autoscaling owner, SRE operabilityPlatform walkthrough
K8sLLM Platform Walkthrough
This code-generated Remotion video explains the production path from cluster isolation and GPU pools to model serving, RAG, GitOps, observability, and cost-aware operations.
100 seconds · 8 architecture scenes · captions includedWhy K8sLLM is different
A practical platform guide, not a collection of toy tutorials.
Architecture-first
Every topic starts with platform boundaries, ownership, failure modes, and operating signals.
Hands-on validation
Interactive labs use terminal-style checks for Kubernetes output, GPU placement, runtime health, and policy evidence.
Production scope
The guide covers GPU capacity, latency, cost, security, GitOps, observability, RAG, and rollback paths.
Source anchored
Pages are tied to official Kubernetes, vLLM, KServe, Ray Serve, and NVIDIA GPU Operator documentation.
What the video covers
Eight architecture scenes for production LLM platforms.
Production LLM Infrastructure on Kubernetes
The opening map frames LLM serving as a platform engineering problem, not just a model deployment.
LLM workloads break normal Kubernetes assumptions
GPU capacity, token latency, queue wait, model rollout, and tenant isolation become first-order design concerns.
Reference Architecture: Production Cluster
Control plane, system pool, app pool, GPU pool, GitOps, policy, backup, and observability are separated by responsibility.
Reference Architecture: LLM Inference Stack
Gateway policy, runtime behavior, GPU placement, inference metrics, and cost signals are shown as one serving path.
Serving Choice: KServe or Ray Serve?
KServe fits standardized platform APIs, while Ray Serve fits programmable Python-native serving graphs.
Reference Architecture: RAG Platform
The RAG map separates ingestion quality from online serving latency and access-controlled retrieval.
Operate it like a platform
GitOps change flow and observability signals make LLM infrastructure reviewable, measurable, and reversible.
K8sLLM
The final map connects production cluster design, GPU node pools, model serving, RAG, observability, GitOps, cost, and reliability.
Learning map
Move from cluster primitives to an AI inference platform.
Challenge lab track
Practice the platform decisions in interactive challenges.
K8sLLM Labs turn each architecture decision into a guided challenge with terminal-style commands, validation checks, progressive hints, expected signals, and failure drills.
Start the vLLM challenge01
vLLM inference challengeDeploy a GPU-backed OpenAI-compatible endpoint and verify TTFT, queueing, placement, and health.
02Hard75 minAI infra
GPU placement, TTFT, queue wait
RAG retrieval challengeValidate tenant-aware retrieval, metadata filters, answer quality, and failure drills.
03Medium60 minMLOps
tenant filters, citations, recall
Production readiness challengeRun launch checks across security, observability, rollback, quota, and cost.
04Hard50 minPlatform lead
RBAC, quota, rollback, dashboards
LLM observability challengeConnect user latency to runtime queueing, token throughput, GPU pressure, traces, and logs.
Medium45 minSRE
TTFT, tokens/sec, GPU pressure
Kubernetes LLM pillars
Start with the pages that shape real AI infrastructure decisions.
K8s LLM GuideUse the exact platform map for Kubernetes LLM infrastructure, teams, and learning paths.vLLM KubernetesDeploy the runtime contract around GPU placement, model cache, health, and metrics.KServe vs Ray ServePick the serving abstraction by ownership model, rollout style, and graph complexity.GPU Node PoolsDesign accelerator pools with taints, quotas, autoscaling, and failure isolation.RAG on KubernetesOperate ingestion, retrieval, generation, and evaluation as one production system.
Built as a product
Built as a production AI infrastructure learning platform.
K8sLLM is designed to show the operating model behind modern AI platforms: runtime selection, GPU scheduling, retrieval quality, policy controls, GitOps delivery, telemetry, rollback, and cost-aware reliability.
Stack coverage
KubernetesvLLMKServeRay ServeRAGPrometheus/GrafanaGitOpspolicy-as-code
Architecture review
Use diagrams as review artifacts, not decoration.
Production cluster blueprintWalk through control plane, worker pools, delivery, policy, observability, and recovery boundaries before adding AI workloads.