vLLM is a high-throughput, memory-efficient inference and serving engine for large language models. Its core innovation is PagedAttention, a novel attention algorithm inspired by virtual memory paging in operating systems, which manages the KV (key-value) cache in non-contiguous memory blocks. This approach eliminates the memory waste caused by fragmentation and reservation in traditional LLM serving systems, achieving near-zero waste of KV cache memory and enabling 2-4x higher throughput compared to naive serving implementations.

vLLM provides an OpenAI-compatible API server out of the box, making it a drop-in replacement for production deployments. It supports continuous batching (dynamically adding and removing requests from running batches), tensor parallelism for distributing models across multiple GPUs, pipeline parallelism for multi-node serving, speculative decoding for faster generation, prefix caching for shared prompt optimization, and quantization methods including AWQ, GPTQ, and FP8. The engine supports a wide range of model architectures from the Hugging Face ecosystem including Llama, Mistral, Mixtral, Qwen, Falcon, and many others.

vLLM has become the industry standard for production LLM serving, used by companies and research labs that need to maximize GPU utilization and minimize inference costs. It is deployed at organizations including Anyscale, Cloudflare, and numerous AI startups. The project integrates with Ray for distributed serving, Kubernetes for orchestration, and is supported by all major GPU cloud providers.

Key Capabilities

  • PagedAttention - Virtual-memory-inspired KV cache management that eliminates memory fragmentation and enables near-optimal GPU memory utilization
  • Continuous Batching - Dynamic batching that adds new requests to running batches without waiting, maximizing GPU throughput
  • Tensor Parallelism - Distribute model layers across multiple GPUs for serving models larger than single-GPU memory
  • OpenAI-Compatible API - Production-ready HTTP server with streaming, function calling, and tool use support matching the OpenAI API format

Cloud Equivalents

vLLM is the open-source alternative to the inference backends powering AWS Bedrock, Azure OpenAI Service, and Google Vertex AI model endpoints. Cloud services abstract away serving infrastructure entirely, while vLLM gives organizations full control over serving optimization, GPU utilization, and cost per token at the expense of managing infrastructure.

Origins and History

vLLM was created by Woosuk Kwon, Zhuohan Li, and other researchers at UC Berkeley’s Sky Computing Lab, with the PagedAttention paper published at SOSP 2023. The project was open-sourced in June 2023 under the Apache License 2.0. It rapidly grew to become the most popular open-source LLM serving framework, accumulating over 30,000 GitHub stars within two years. The project is governed by a community of contributors from UC Berkeley, Anyscale, and numerous industry partners.

Sources

  1. https://github.com/vllm-project/vllm
  2. Kwon, W. et al. “Efficient Memory Management for Large Language Model Serving with PagedAttention.” SOSP, 2023.