Deploying AI Models at Scale with Kubernetes: Complete MLOps Guide (2026)

Kubernetes has become the standard substrate for serving ML and LLM models at scale, because the hard parts — autoscaling, rolling/canary updates, GPU scheduling, multi-replica reliability — are exactly what it's built for. This guide covers the serving frameworks and the production concerns that matter.

Model-serving frameworks

KServe (formerly KFServing): Kubernetes-native serving with autoscaling (including scale-to-zero), canary rollouts, and a standard inference protocol; supports many runtimes (PyTorch, TF, sklearn, custom containers, and LLM runtimes).

Seldon Core: flexible serving with inference graphs (chain models, transformers, explainers) — strong when you need multi-step pipelines.

vLLM / TGI on K8s: for LLMs specifically, run a high-throughput engine (see LLM 推理优化) as a Deployment behind a Service, scaled by GPU.

The production concerns

GPU scheduling. Request GPUs explicitly (nvidia.com/gpu: 1), use node selectors/taints to pin model pods to GPU nodes, and consider MIG/time-slicing to share big GPUs across small models.

Autoscaling. CPU/memory HPA is the wrong signal for inference — scale on GPU utilization, queue depth, or request latency (via KEDA or custom metrics).

Canary deployments. Roll a new model version to a small traffic slice, watch quality/latency, then ramp — the same idea as AI Canary Analysis.

Cold starts. Large models load slowly; pre-pull images, keep warm replicas, or use scale-to-zero only where startup latency is acceptable.

Multi-region. For global latency and resilience, replicate across regions — see Multi-Region AI Deployment.

yaml
A minimal KServe InferenceService
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata: { name: my-llm }
spec:
  predictor:
    containers:
      - image: vllm/vllm-openai:latest
        args: ["--model", "meta-llama/Llama-3.1-8B-Instruct"]
        resources: { limits: { nvidia.com/gpu: "1" } }

Don't forget observability

Track latency percentiles, GPU utilization, tokens/sec, and error/fallback rates. Pair serving with evaluation and tracing — see LangSmith for evaluation — so a "successful" deploy that quietly degrades quality gets caught.

FAQ

KServe or Seldon? KServe for standard autoscaling serving; Seldon when you need inference graphs/pipelines. How do I autoscale GPU workloads? On GPU utilization / queue depth / latency via KEDA or custom metrics — not CPU. How to update safely? Canary: small traffic slice, watch metrics, then ramp. Can pods scale to zero? Yes (KServe), but mind cold-start latency for large models.

Summary

Kubernetes gives ML serving the autoscaling, rollouts, and GPU scheduling production needs. Use KServe (or vLLM-on-K8s for LLMs), scale on inference-relevant metrics, ship with canaries, plan for cold starts and multi-region, and wire up observability so quality regressions surface fast.

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*Last updated: June 2026. Verify against the KServe/Seldon and Kubernetes docs.*