serving-llms-vllm
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible end
- Skills
- AI research
- 2
What it does and how to use it
A skill that teaches Claude Code to do ai research tasks well. It is packaged knowledge the model uses whenever it is relevant.
- Install the skill (command above).
- In Claude Code, describe what you need - the skill turns on by itself when the context fits.
- No commands to memorize: you talk in plain language and the skill does the rest.
Install (Claude Code)
mkdir -p .claude/skills/serving-llms-vllm && curl -fsSL https://doutorai.pt/data/lib/skills__serving-llms-vllm.md -o .claude/skills/serving-llms-vllm/SKILL.mdPaste this command into the terminal, in your project folder, and the component installs automatically.
Content
---
name: serving-llms-vllm
description: Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [vLLM, Inference Serving, PagedAttention, Continuous Batching, High Throughput, Production, OpenAI API, Quantization, Tensor Parallelism]
dependencies: [vllm, torch, transformers]
---
# vLLM - Hig
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