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vllm

Use when self-hosting an open-weight LLM for high-throughput concurrent serving with vLLM — running an OpenAI-compatible endpoint, splitting a model across GPUs with tensor or pipeline parallelism, loading quantized weights, serving one or many LoRA adapters, and debugging KV-cache OOM from memory-utilisation and context-length flags. NOT renting or provisioning the GPU box (that is `runpod` or `modal`), NOT single-user laptop inference (that is `ollama`), NOT a hosted inference API you do not operate (that is `together-fireworks` or `huggingface`).

skills/vllm/SKILL.md

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# vLLM — high-throughput serving of open-weight models

vLLM is the inference **engine** you put in front of an open-weight model when many requests hit it at
once. Its job — and this skill's — is throughput under concurrency: keep the GPU busy across dozens of
simultaneous requests, not squeeze one prompt out fast. You own the `vllm serve` flags; the box those
flags run on is a `runpod`/`modal` concern.

**Why not just loop a `transformers` `generate()`?** Naive serving runs one request at a time and pads
every batch to the longest sequence, so the GPU idles. vLLM fixes both:

- **PagedAttention** stores the KV cache in non-contiguous fixed-size blocks (like OS virtual-memory
  paging), so there is almost no padding/reservation waste and long contexts pack tightly.
- **Continuous batching** admits and retires requests token-by-token instead of per-batch, so a new
  request joins the running batch immediately rather than waiting for the slowest one to finish.