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agentsop-vllm

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Decision SOP for serving LLMs with vLLM. Covers PagedAttention mental model, quantization/parallelism/batching tradeoffs, OOM triage, and when NOT to use vLLM. Activates when a coder-agent is choosing or tuning an inference engine, debugging vLLM throughput/latency/OOM, or comparing vLLM against TGI/SGLang/TensorRT-LLM/llama.cpp.

skills/agentsop-vllm/SKILL.md

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Oct 24, 2026
License
MIT

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# vLLM Serving SOP

## 1. 何时激活 (When to activate)

Activate this skill when any of the following hold:

- The user wants to **serve an LLM in production** (multi-user, concurrent requests, throughput-oriented) and has GPU infrastructure.
- The user is **comparing inference engines** (vLLM vs TGI vs SGLang vs TensorRT-LLM vs llama.cpp/Ollama).
- The user reports a **vLLM operational issue**: CUDA OOM, low throughput, high TTFT, request preemption, multi-GPU setup, quantization choice.
- The user is **sizing hardware** for an open-weights model (Llama / Qwen / Mixtral / DeepSeek-V3) and asking about tensor/pipeline parallelism.
- The user mentions PagedAttention, prefix caching, continuous batching, chunked prefill, or speculative decoding.

**Do NOT activate** for: training/fine-tuning (use accelerate/deepspeed/trl), CPU-only edge inference (use llama.cpp/Ollama), Apple Silicon production (vLLM Metal/MPS is experimental, not production-ready as of 2026) [aimadetools.com 2026], API-only consumption of hosted models (just call the OpenAI/Anthropic SDK).