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vllm - Operate and troubleshoot vLLM inference servers

Deploy, configure, serve, benchmark, tune, and troubleshoot vLLM inference servers across Docker, Kubernetes, GPUs, APIs, and upgrades.

Tags

Updated: 2026-10-01
vLLMinference servingDockerKubernetesOpenAI APIbenchmarkingGPU operationscontinuous batchingquantizationtroubleshooting

Capabilities

Deploy vLLM with DockerDeploy vLLM on KubernetesConfigure model servingTune KV cache and batching

Typical Inputs

Deployment targetvLLM version or image digestModel and revision

Typical Outputs

Deployment configuration changesOpenAI-compatible API responsesHealth and metrics probe JSON

What this skill does

  • Deploy vLLM with Docker
  • Deploy vLLM on Kubernetes
  • Configure model serving
  • Tune KV cache and batching
  • Serve OpenAI-compatible APIs
  • Benchmark throughput and latency
  • Inspect health and metrics
  • Diagnose GPU and startup failures
  • Upgrade or roll back deployments
  • Verify inference delivery

Inputs

  • Deployment target
  • vLLM version or image digest
  • Model and revision
  • Quantization and parallelism settings
  • GPU inventory
  • Workload specification
  • Benchmark dataset
  • Server URL
  • Human mutation directive

Outputs

  • Deployment configuration changes
  • OpenAI-compatible API responses
  • Health and metrics probe JSON
  • Benchmark metrics and run records
  • Upgrade or rollback status
  • Diagnostic summaries

Requirements

  • vLLM 0.26.0 or pinned older release
  • Supported NVIDIA CUDA, AMD ROCm, Intel XPU, or CPU environment
  • Matching GPU driver when applicable
  • Python 3.9+ for the health script
  • HTTP(S) access for live probes
  • Human confirmation for mutations

Source

  • Spec: SKILL.md

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