vLLM

✓ Editorially verified

Open-source high-throughput inference engine for serving LLMs with PagedAttention and continuous batching.

Free· Free and open-source (Apache 2.0); self-hosted infrastructure costs applyFine-tuningMulti-model (open-weight LLMs: Llama, Qwen, DeepSeek, Mistral, Gemma, Phi, etc.)

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Best for

Pick vLLM if you are self-hosting open-weight LLMs at any meaningful scale and need an OpenAI-compatible endpoint with maximum tokens-per-dollar.

Skip if

Skip it if you don't run your own GPUs or you'd rather pay a managed inference provider than tune batching, parallelism, and KV cache yourself.

vLLM is an open-source inference and serving engine purpose-built for running large language models at scale. Its headline innovation is PagedAttention, a memory-management technique that treats the KV cache like virtual memory pages, dramatically reducing fragmentation and letting a single GPU serve far more concurrent requests than naive transformer implementations. Continuous batching, speculative decoding, tensor and pipeline parallelism, and quantization (AWQ, GPTQ, FP8) are all first-class.

The target audience is teams self-hosting open-weight models (Llama, Qwen, DeepSeek, Mistral, Mixtral, Gemma, Phi, etc.) who want OpenAI-compatible endpoints without paying per-token API rates. vLLM ships a drop-in OpenAI-compatible HTTP server, so existing client code generally works with a base-URL swap. It's free and community-driven, originally from UC Berkeley's Sky Computing Lab and now backed by compute sponsorships from NVIDIA, AWS, Google Cloud and others.

Hardware coverage is unusually broad: NVIDIA CUDA, AMD ROCm, Intel CPU/GPU/Gaudi, AWS Neuron, TPU, and Apple Silicon are all supported to varying degrees. The trade-off is operational: you bring the GPUs, the Kubernetes/Ray cluster, and the on-call rotation. Documentation is solid but assumes ML-infra fluency.

Editor's take

vLLM is effectively the default open-source serving layer for self-hosted LLMs in 2026 — if you've used a fast OSS inference endpoint anywhere, there's a decent chance vLLM was underneath. The throughput gains over naive HuggingFace serving are not marginal; they're the difference between one GPU and four. Just don't underestimate the ops burden.

— The AI Tool Bible editorial team