Running your own LLMs on Kubernetes isn't just a cost play — it's about latency, data sovereignty, and fine-tuning control. But GPU scheduling at scale is a different beast entirely.
Here's what a production K8s LLM inference stack looks like in 2026: vLLM or TGI for the inference server, NVIDIA GPU Operator for driver management, KEDA for request-based autoscaling, and spot instances for dev/staging environments to cut costs by 60-70%.
The numbers matter: a single A100-80GB can serve Llama 3 70B with vLLM at ~30 tokens/second for 4 concurrent users. With continuous batching, that jumps to 8-10 users. But cold starts are brutal — 45-90 seconds for large models — which is why you need keep-warm pods and predictive scaling.
My article covers the complete architecture: GPU node pool setup, vLLM deployment manifests, HPA vs KEDA tradeoffs, model caching strategies with PersistentVolume, and cost optimization with spot/preemptible instances.
Get the full deployment guide with working YAML manifests at https://devtocash.com/blog/kubernetes-llm-inference-deploy-scale-2026
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