Last Updated: 3/19/2026

Overview

vLLM is a fast and easy-to-use library for large language model (LLM) inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

What is vLLM?

vLLM is designed to make LLM serving easy, fast, and cheap for everyone. It provides a high-throughput, memory-efficient inference engine that can handle production workloads at scale.

Why vLLM?

PagedAttention for Efficient Memory Management

At the core of vLLM is PagedAttention, a novel attention algorithm that efficiently manages attention key and value memory. This innovation enables vLLM to achieve state-of-the-art serving throughput while minimizing memory waste.

High-Performance Serving

vLLM delivers exceptional performance through:

• Continuous batching of incoming requests for maximum throughput
• Fast model execution with CUDA/HIP graph optimization
• Optimized CUDA kernels with FlashAttention and FlashInfer integration
• Speculative decoding for faster generation
• Chunked prefill to reduce latency

Flexible Quantization Support

vLLM supports multiple quantization methods to reduce memory footprint and increase throughput:

• GPTQ, AWQ, and AutoRound
• INT4, INT8, and FP8 precision

Distributed Inference

For large models, vLLM supports tensor parallelism, pipeline parallelism, data parallelism, and expert parallelism to distribute inference across multiple GPUs or nodes.

Key Capabilities

OpenAI-Compatible API Server

vLLM provides an HTTP server that implements the OpenAI API specification, making it a drop-in replacement for OpenAI’s API. This allows you to use existing OpenAI client libraries and tools with your self-hosted models.

Offline Batch Inference

The LLM class provides a simple Python interface for running batch inference without spinning up a server. This is ideal for offline processing, evaluation, and experimentation.

Broad Hardware Support

vLLM runs on diverse hardware platforms:

• NVIDIA GPUs (primary platform with CUDA)
• AMD GPUs (with ROCm)
• Intel CPUs and GPUs
• Google TPUs
• PowerPC CPUs and Arm CPUs
• Hardware plugins for Intel Gaudi, IBM Spyre, and Huawei Ascend

Extensive Model Compatibility

vLLM seamlessly supports most popular open-source models from Hugging Face:

• Transformer-like LLMs such as Llama, Mistral, Qwen, and Gemma
• Mixture-of-Expert models such as Mixtral and Deepseek-V2/V3
• Embedding models such as E5-Mistral
• Multi-modal LLMs such as LLaVA for vision-language tasks

Advanced Features

• High-throughput serving with parallel sampling, beam search, and other decoding algorithms
• Streaming outputs for real-time response generation
• Prefix caching to accelerate repeated prompt patterns
• Multi-LoRA support for serving multiple fine-tuned adapters simultaneously

Who Should Use vLLM?

vLLM is designed for:

• ML engineers building LLM-powered applications that require high throughput and low latency
• Platform teams deploying and managing LLM inference infrastructure at scale
• Researchers conducting experiments and evaluations with large language models
• Organizations seeking cost-effective alternatives to commercial LLM APIs

Whether you’re serving a single model to a few users or running a multi-tenant platform with thousands of requests per second, vLLM provides the performance and flexibility you need.

What’s Next

• Installation: Install vLLM on NVIDIA CUDA, AMD ROCm, Google TPU, or other hardware using pip, uv, or conda.
• Quick Start: Run your first inference in under 5 minutes — both offline batch mode and the OpenAI-compatible server.