🧩 AI FrameworksOpen SourceFree

vLLM

High-throughput LLM serving — PagedAttention, continuous batching

4.7/ 5⭐ 45,000 GitHub starsPythonApache-2.0

📋 Overview

vLLM is a high-throughput LLM serving engine that uses PagedAttention and continuous batching to maximize GPU utilization. Unlike Ollama (which focuses on simplicity), vLLM focuses on performance — delivering the highest throughput for production deployments. It supports any HuggingFace model, tensor parallelism, and multiple quantization schemes.

✨ Key Features

•35K+ GitHub stars — highest-throughput serving
•PagedAttention for memory efficiency
•Continuous batching for throughput
•Any HuggingFace model
•Tensor parallelism
•Apache 2.0 licensed

🎯 The Problem It Solves

Production LLM serving requires high throughput and GPU efficiency. vLLM delivers this with PagedAttention (memory optimization) and continuous batching (throughput optimization).

🔧 How It Works

vLLM uses PagedAttention to manage KV cache memory efficiently — paging memory like an OS, eliminating fragmentation. Continuous batching groups requests for maximum throughput. It supports tensor parallelism, quantization, and any HuggingFace model.

🚀 Installation & Quick Start

Installation

pip install vllm

Quick Start

pip install vllm
python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3-8B
API at localhost:8000

✅ Pros

•Highest throughput serving
•PagedAttention memory optimization
•Any HuggingFace model
•Tensor parallelism
•Apache 2.0 licensed
•Active community

❌ Cons

•Complex setup
•Requires significant GPU
•API less feature-rich than OpenAI

💬 Practitioner Verdict

“vLLM is the highest-throughput open-source LLM serving engine — the PagedAttention is genuinely differentiated. The trade-off: complex setup, requires significant GPU resources, and the API is less feature-rich than OpenAI. For production serving that needs maximum throughput, vLLM is the default.”