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vLLM

vLLM

AI/ML Self-hostable Apache-2.0
pythonllminferenceself-hostedgpuservingopenai-compatible

When to use this

Use this when you need to serve LLMs at high throughput on your own GPUs. Much faster than naively running HuggingFace transformers — handles multiple concurrent requests efficiently via PagedAttention. Production-grade LLM serving.

View on GitHub Live Site / Docs

YouTube Tutorials

Click any card to watch on YouTube

vLLM Tutorial: Blazing Fast LLM Inference Server
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vLLM Tutorial: Blazing Fast LLM Inference Server

Deploying Local LLMs at Scale using vLLM
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Deploying Local LLMs at Scale using vLLM

vLLM PagedAttention Explained - Beginner's Guide
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vLLM PagedAttention Explained - Beginner's Guide

What is vLLM?

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to serve LLMs at production scale with high concurrency and low latency.

Why vLLM?

The core innovation is PagedAttention — a memory management algorithm that allows serving more concurrent requests by managing GPU KV-cache memory like an OS manages RAM pages. This means:

  • 24x higher throughput than naive HuggingFace serving on the same hardware.
  • Continuous batching — new requests join an in-flight batch as slots free up.
  • Efficient memory — near-zero memory waste on KV cache.

Key Features

  • OpenAI-compatible API — drop-in replacement for OpenAI's API server.
  • Streaming — supports streaming token output.
  • Multi-GPU — tensor parallelism across multiple GPUs.
  • Many models — supports LLaMA, Mistral, Mixtral, Falcon, MPT, GPT-2, T5, and more.
  • Quantization — AWQ, GPTQ, SqueezeLLM.

Quick Start

pip install vllm
python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-8B-Instruct

vs. Ollama

Ollama is for personal use on a laptop. vLLM is for serving LLMs to many users with enterprise-grade throughput. Choose vLLM when you're building a product that needs to serve hundreds of concurrent requests.

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