K8s LLM: Kubernetes LLM Platform Guide

K8sLLM is a senior platform engineering guide for building a Kubernetes LLM platform. It connects Kubernetes primitives, GPU capacity, model serving, RAG systems, observability, security, and hands-on labs into one operating model.

The goal is practical: help a platform team design and run LLM on Kubernetes without treating inference as only an application deployment problem. A production K8s LLM platform needs scheduling policy, runtime contracts, rollout strategy, tenant controls, cost signals, and incident-ready telemetry.

Who this guide is for

Role	What you should get from K8sLLM
Platform architect	A decision map for Kubernetes LLM infrastructure and platform services.
AI infrastructure engineer	Runtime guidance for vLLM, KServe, Ray Serve, GPU pools, RAG, and benchmarking.
SRE or production engineer	Failure modes, validation signals, and launch-readiness checks.
Engineering leader	A roadmap for turning LLM experiments into a governed platform capability.

The Kubernetes LLM platform map

Layer	Platform decision
Cluster baseline	Control plane reliability, worker pool boundaries, network policy, storage, backup, and admission policy.
GPU capacity	Node pool labels, taints, tolerations, device plugin, GPU Operator, quotas, and autoscaling buffers.
Model runtime	vLLM, Triton, or another runtime that owns model loading, batching, KV cache, streaming, and health.
Serving abstraction	KServe, Ray Serve, or a direct deployment model depending on team ownership and serving graph complexity.
RAG platform	Ingestion jobs, embedding services, vector database, metadata filters, rerankers, evaluation, and feedback loops.
Observability	TTFT, inter-token latency, queue wait, output tokens/sec, GPU saturation, retrieval quality, and cost/request.
Security	Identity, tenant routing, secrets, model access, egress policy, prompt logging controls, and supply chain review.

Start here

1. Read LLM on Kubernetes for the overall production model.
2. Design accelerator capacity with GPU Node Pool Kubernetes.
3. Use vLLM on Kubernetes when the main problem is high-throughput text generation.
4. Compare platform abstractions in KServe vs Ray Serve.
5. Use Model Serving Options to compare vLLM, KServe, Ray Serve, and Triton.
6. Build retrieval systems with RAG on Kubernetes.
7. Validate the path with the Kubernetes LLM Labs.

What makes a K8s LLM platform production-grade

Question	Production answer
Can the cluster place the workload predictably?	GPU workloads use explicit node labels, taints, tolerations, resource limits, and capacity reservations.
Can the runtime survive real traffic?	Probes, metrics, model cache behavior, streaming behavior, and cold-start latency are tested before rollout.
Can the team compare serving options?	The platform has a decision matrix for vLLM, KServe, Ray Serve, Triton, and custom deployments.
Can incidents be debugged?	Dashboards connect gateway latency, runtime queueing, GPU pressure, retrieval calls, and model output behavior.
Can cost be explained?	Reports include request class, input tokens, output tokens, GPU profile, utilization, and cache behavior.

Pillar pages

Pillar	Primary keyword	Use it for
vLLM on Kubernetes	vLLM Kubernetes	Runtime deployment, GPU scheduling, model cache, probes, and metrics.
KServe vs Ray Serve	KServe vs Ray Serve	Ownership model, CRDs, serving graph complexity, autoscaling, and rollouts.
GPU Node Pool Kubernetes	GPU node pool Kubernetes	Accelerator placement, quotas, autoscaling, and isolation.
RAG on Kubernetes	RAG on Kubernetes	Ingestion, retrieval, vector databases, evaluation, and failure modes.
Inference Benchmarking and Cost Model	Kubernetes LLM cost model	Latency phases, throughput, GPU economics, and benchmark design.

Hands-on lab path

The labs make K8sLLM more than a reading site. Each lab includes objective, prerequisites, manifests or commands, validation signals, failure drills, and expected signals.

Lab	Practice
vLLM inference lab	Deploy a GPU-backed OpenAI-compatible endpoint and inspect token latency.
RAG retrieval lab	Operate ingestion, retrieval, answer quality, and failure drills.
Production readiness lab	Review rollout, security, quota, cost, rollback, and observability gates.
Observability lab	Build signals for TTFT, queue wait, GPU pressure, traces, logs, and alerts.

Reference architectures

Use the architecture pages as review artifacts:

• Production Kubernetes Cluster
• LLM Inference Stack
• RAG Platform
• Multi-Tenant Security
• Observability Pipeline

Editorial stance

K8sLLM is not a vendor ranking site. The content starts from official project documentation, then adds platform engineering decisions, failure modes, and field checklists. See About K8sLLM and the Content Review Checklist for how pages are reviewed.