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serving-llms-vllm

Serve LLM models with OpenAI-compatible endpoints using vLLM.

Updated May 25, 2026
Search Tags:#performance#OpenAI#LLM#quantization#inference
One-click install
npx skills add https://github.com/zaiyemeiyou404/Hermes --skill serving-llms-vllm-zaiyemeiyou404
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: serving-llms-vllm
Source: https://github.com/zaiyemeiyou404/Hermes/tree/main/backup/skills/mlops/inference/vllm
Command: npx skills add https://github.com/zaiyemeiyou404/Hermes --skill serving-llms-vllm-zaiyemeiyou404

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires vllm, torch, transformers, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill unit addresses the challenge of deploying production-level LLM APIs, focusing on optimizing inference latency and throughput, particularly for models with limited GPU memory.

Core Features & Use Cases

  • High Throughput Inference: Achieves up to 24x higher throughput than standard transformers using PagedAttention and continuous batching.
  • OpenAI Compatibility: Supports OpenAI-compatible endpoints for seamless integration with OpenAI's infrastructure.
  • Quantization and Memory Efficiency: Implements quantization techniques (GPTQ/AWQ/FP8) to fit large models within limited GPU memory.
  • Tensor Parallelism: Utilizes tensor parallelism for further efficiency on multi-GPU setups.

Quick Start

Install vLLM and serve a LLM model:

pip install vllm
vllm serve meta-llama/Llama-3-8B-Instruct

Frequently Asked Questions about serving-llms-vllm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I improve LLM inference throughput for an OpenAI compatible API?▼

You can improve LLM inference throughput for an OpenAI compatible API by using vLLM to implement PagedAttention and continuous batching, achieving up to 24x higher throughput than standard transformers.

How do I deploy a large LLM when limited by GPU memory?▼

To deploy a large LLM with limited GPU memory, apply quantization techniques like GPTQ, AWQ, or FP8. This compresses the model weights to fit within your hardware constraints while maintaining inference performance.

Can I run LLM inference on a multi-GPU setup with vLLM?▼

Yes, you can run LLM inference on a multi-GPU setup with vLLM by utilizing tensor parallelism. This splits the model tensors across multiple GPUs to maximize computational efficiency and memory distribution.

What is the best way to serve an OpenAI API endpoint from an open source model?▼

The best way to serve an OpenAI API endpoint from an open source model is using vLLM. It provides OpenAI-compatible endpoints for seamless infrastructure integration, initiated via the `vllm serve` command.

Does vLLM work on macOS for LLM serving?▼

Yes, vLLM supports macOS for LLM serving alongside Linux. You can install it via pip and use it to serve models like meta-llama/Llama-3-8B-Instruct directly on your local environment.

Why use PagedAttention for high-throughput LLM serving?▼

PagedAttention is used for high-throughput LLM serving because it optimizes memory management during inference. Combined with continuous batching, it significantly reduces latency and maximizes request processing volume.

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