head to head · open source

llama.cpp vs OpenLLM

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. OpenLLM has 12,535 stars, 841 forks, 20 open issues and last shipped 4 days ago. llama.cpp leads on adoption by 926% (128,581 vs 12,535 stars). llama.cpp is written in C++ under MIT; OpenLLM is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, OpenLLM 7%. llama.cpp 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 ★ 129K OpenLLM ★ 13K category AI & Machine Learning

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Side by side

	llama.cpp	OpenLLM
GitHub stars	★ 129K	★ 13K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-17	2026-09-14
Forks	⑂ 23K	⑂ 841
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 129K 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 OpenLLM if

You want the OpenLLM 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 OpenLLM fits your workflow better

full OpenLLM profile →

About llama.cpp

llama.cpp is a C/C++ library and set of command line tools for running large language models (LLMs) and vision language models (VLMs) locally. It enables inference without external dependencies, targeting diverse hardware including Apple Silicon, x86 CPUs, NVIDIA GPUs (via CUDA), AMD GPUs (via HIP), and other accelerators. The project lives in the ggml ecosystem, leveraging the ggml tensor computation library for low level operations and quantized model execution.

read the full llama.cpp overview →

About OpenLLM

OpenLLM is an Apache 2.0 Python project from BentoML that runs open source or custom large language models as OpenAI compatible API endpoints on self hosted infrastructure, and it is aimed at developers and platform teams who want to serve Llama, Qwen, Mistral, DeepSeek and similar models in the cloud without rewriting their client code.

read the full OpenLLM overview →

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Frequently asked questions

Is llama.cpp or OpenLLM more popular?

llama.cpp has 128,581 GitHub stars and OpenLLM has 12,535. llama.cpp has the larger community by that measure.

Are llama.cpp and OpenLLM free?

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

What is the difference between llama.cpp and OpenLLM?

llama.cpp is written in C++ and OpenLLM 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 OpenLLM?

Choose llama.cpp if you want the larger community (128,581 stars) or its MIT licence terms. Choose OpenLLM if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.