vLLM

What is vLLM?

vLLM is an open-source library for running large language model inference and serving. It began in the Sky Computing Lab at UC Berkeley and is now maintained by a community of over 2000 contributors. Its throughput comes from PagedAttention, which manages attention key and value memory efficiently, together with continuous batching of incoming requests, chunked prefill and prefix caching. The same engine can load 200+ model architectures from Hugging Face, from decoder-only and mixture-of-experts models to multimodal and embedding models, and it exposes an OpenAI-compatible API server so existing clients can point at your own endpoint. Hardware support covers NVIDIA and AMD GPUs and x86, ARM and PowerPC CPUs, with plugins for TPUs, Gaudi and other accelerators. ML engineers and platform teams use it to cut serving cost and latency on their own infrastructure. vLLM is free to install, for example with uv pip install vllm, and there are no paid plans, since you bring your own compute.

Use cases

• Serve open-source LLMs behind an API
• Self-host an OpenAI-compatible endpoint
• Cut GPU cost for model inference
• Run multi-LoRA model deployments
• Serve embedding and retrieval models
• Run distributed inference across GPUs
• Benchmark new open models on release

Who it's for

• ML engineers
• Platform teams
• AI researchers
• Backend developers

Why use vLLM

• Higher request throughput from the same GPUs through continuous batching and PagedAttention.
• Existing OpenAI-style clients work against your own endpoint with little code change.
• One engine covers many model families and several hardware vendors, so you avoid tooling per model.
• Free and open source, so cost is limited to the compute you run it on.
• New models and optimizations show up quickly, with release notes and a blog on the site.

vLLM features

• PagedAttention for attention key and value memory
• Continuous batching, chunked prefill and prefix caching
• OpenAI-compatible API server
• Anthropic Messages API and gRPC support
• Quantization including FP8, NVFP4, INT8, INT4, GPTQ and AWQ
• Speculative decoding with n-gram, suffix and EAGLE
• Tensor, pipeline, data, expert and context parallelism
• Structured outputs via xgrammar or guidance
• Tool calling and reasoning parsers
• Multi-LoRA support for dense and MoE layers
• Disaggregated prefill, decode and encode
• Streaming outputs

vLLM integrations

• Hugging Face
• PyTorch
• FlashAttention
• FlashInfer
• Docker
• OpenAI API clients

How to get started with vLLM

1. Install vLLM with the selector on vllm.ai, for example uv pip install vllm --torch-backend auto, using Python 3.10 or newer.
2. Pick a model from Hugging Face that matches one of the supported architectures.
3. Start the OpenAI-compatible API server on your GPU or CPU machine.
4. Point your application or an OpenAI client at the endpoint and send requests.
5. Tune parallelism, quantization, speculative decoding and LoRA settings from the docs for your hardware.

vLLM limitations

• You supply and manage your own GPUs or CPUs
• Requires Python 3.10 or newer
• Some features are incompatible with each other or with certain hardware per the compatibility matrix

vLLM pricing

vLLM is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

vLLM alternatives

The main vLLM alternatives listed on Debuly are SGLang, llama.cpp, Ollama, FastChat, Fireworks AI and Novita AI. The table compares pricing and platforms; order is A–Z.

1. 

   FastChat

   Open platform for training, serving and evaluating LLM chatbots, with an OpenAI-compatible API and the code behind Chatbot Arena.

   Developer platformsChat assistantsCoding
   Open sourceVisit FastChat (official site)
2. 

   Fireworks AI

   Inference and training platform for open models: pay-per-token serverless APIs, GPU deployments, fine-tuning and reserved capacity.

   Developer platformsCodingAutomation and agents
   PaidVisit Fireworks AI (official site)
3. 

   llama.cpp

   Open-source engine and desktop app for running LLMs locally, with a built-in OpenAI-compatible API server and web chat UI.

   Developer platformsCodingChat assistants
   Open sourceVisit llama.cpp (official site)
4. 

   Novita AI

   AI cloud with pay-per-token model APIs for 200+ models, dedicated GPU endpoints, agent sandboxes and GPU rentals.

   Developer platformsCodingAutomation and agents
   PaidVisit Novita AI (official site)
5. 

   Ollama

   Open-source app and CLI for running open AI models locally on macOS, Windows and Linux, with optional paid cloud models.

   Developer platformsCodingChat assistants
   Open sourceVisit Ollama (official site)
6. 

   SGLang

   Open-source serving framework for LLMs and multimodal models, with OpenAI-compatible endpoints and multi-GPU deployment.

   Developer platformsCoding
   Open sourceVisit SGLang (official site)

vLLM FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from vllm.ai · fetched 24 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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