head to head · open source

llama.cpp vs lorax

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. lorax has 3,832 stars, 324 forks, 189 open issues and last shipped 4 months ago. llama.cpp leads on adoption by 3,255% (128,581 vs 3,832 stars). llama.cpp is written in C++ under MIT; lorax is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, lorax 8%. 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 lorax ★ 3.8K category AI & Machine Learning

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Side by side

	llama.cpp	lorax
GitHub stars	★ 129K	★ 3.8K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-17	2026-05-28
Forks	⑂ 23K	⑂ 324
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 lorax if

You want the lorax 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 lorax fits your workflow better

full lorax 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 lorax

LoRAX, short for LoRA eXchange, is an open source Python framework for serving many fine tuned large language models from a single GPU. It lives in machine learning infrastructure and uses base models with LoRA adapters. The base model is shared across requests, while task specific adapter weights are loaded dynamically for each request.

read the full lorax overview →

More in AI & Machine Learning

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Frequently asked questions

Is llama.cpp or lorax more popular?

llama.cpp has 128,581 GitHub stars and lorax has 3,832. llama.cpp has the larger community by that measure.

Are llama.cpp and lorax free?

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

What is the difference between llama.cpp and lorax?

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

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