vLLM is a fast, easy-to-use library for large language model inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, it has grown into one of the most active open-source AI projects, maintained by a community of more than 2,000 contributors and carrying over 83,000 GitHub stars under the Apache 2.0 license. It has become a de facto standard for self-hosting LLMs at high throughput. ## PagedAttention and Memory Efficiency vLLM's signature innovation is PagedAttention, a technique that manages attention key and value memory the way an operating system manages virtual memory with pages. This nearly eliminates the memory fragmentation and over-allocation that plague naive KV-cache implementations, allowing many more requests to share a GPU. Combined with continuous batching of incoming requests, chunked prefill, and prefix caching, it delivers state-of-the-art serving throughput on the same hardware. ## Broad Model and Hardware Support vLLM seamlessly supports over 200 model architectures from Hugging Face, spanning decoder-only LLMs like Llama, Qwen, and Gemma, mixture-of-expert models such as Mixtral and DeepSeek-V3, hybrid state-space models, and multimodal and embedding models. On the hardware side it runs on NVIDIA and AMD GPUs, x86/ARM/PowerPC CPUs, and through plugins reaches Google TPUs, Intel Gaudi, AWS, Apple Silicon, and more, giving teams flexibility in where they deploy. ## Production-Grade Serving Features The project is built for real deployments. It exposes an OpenAI-compatible API server, plus Anthropic Messages API and gRPC support, so it can slot into existing tooling with minimal changes. It offers tensor, pipeline, data, expert, and context parallelism for distributed inference, streaming outputs, structured output generation via xgrammar or guidance, tool calling and reasoning parsers, and efficient multi-LoRA support for serving many fine-tuned adapters at once. ## Performance Engineering Depth Under the hood vLLM bundles an extensive set of optimizations: a wide range of quantization formats including FP8, INT8/INT4, GPTQ, AWQ, GGUF, and NVFP4; optimized attention kernels such as FlashAttention and FlashInfer; speculative decoding methods like n-gram and EAGLE; CUDA and HIP graph execution; and torch.compile-driven graph transformations. Disaggregated prefill and decode further help operators tune latency and throughput for their specific workloads. ## Ecosystem and Momentum VLLM is updated continuously, with daily commit activity reflecting its large contributor base and rapid adoption of new models and hardware backends. Its documentation, blog, forum, and developer Slack support a broad ecosystem, and major model releases are frequently accompanied by day-zero vLLM support. For organizations standardizing their inference stack, this momentum reduces the risk of betting on a tool that might stall. ## Considerations vLLM is optimized first for GPU serving, so squeezing the best throughput typically assumes capable accelerator hardware, and its rich feature surface means there are many knobs to tune for a given workload. The pace of development is a strength but also means APIs and defaults evolve quickly, so teams should pin versions and track release notes. For simple single-user local experimentation, lighter-weight runtimes may be easier to start with, though vLLM scales far better as load grows.