head to head · open source

llama.cpp vs vllm

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. vllm has 96,158 stars, 24,317 forks, 7,953 open issues and last shipped yesterday. llama.cpp leads on adoption by 39% (133,181 vs 96,158 stars). llama.cpp is written in C++ under MIT; vllm is written in Python under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, vllm 25%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee.

Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.

llama.cpp ★ 133K vllm ★ 96K category AI & Machine Learning

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Side by side

	llama.cpp	vllm
GitHub stars	★ 133K	★ 96K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 24K
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 133K stars and counting
You want the MIT license terms
Your stack matches C++
You value the larger contributor base for long-term maintenance

full llama.cpp profile →

pick vllm if

You want the vllm feature set and don't need the biggest community
You prefer the Apache-2.0 license terms
Your stack matches Python
You evaluated both and vllm fits your workflow better

full vllm profile →

About llama.cpp

llama.cpp LLM inference in C/C++ ggml / ops / maintainer PRs%20sort%3Aupdated desc) / dev stats / lib llama API / llama server REST API Quick start A few options to get llama.cpp installed on your machine: Visit and follow the instructions Run with Docker see our Docker documentation Download pre built binaries from the releases page Build from source by cloning this repository check out our build guide Once installed: VLM session with llama cli Built in web UI against llama serve Description The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state of the art performance on a…

read the full llama.cpp overview →

About vllm

Easy, fast, and cheap LLM serving for everyone 🔥 We have built a vLLM website to help you get started with vLLM. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us. About vLLM is a fast and easy to use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors. vLLM is fast with: State of the art serving throughput Efficient management of…

read the full vllm overview →

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Frequently asked questions

Is llama.cpp or vllm more popular?

llama.cpp has 133,181 GitHub stars and vllm has 96,158. llama.cpp has the larger community by that measure.

Are llama.cpp and vllm free?

Both are open source. llama.cpp is licensed under MIT and vllm under Apache-2.0. Neither carries a licence fee.

What is the difference between llama.cpp and vllm?

llama.cpp is written in C++ and vllm in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.

Which should I choose, llama.cpp or vllm?

Choose llama.cpp if you want the larger community (133,181 stars) or its MIT licence terms. Choose vllm if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.