head to head · open source

llama.cpp vs serviette

llama.cpp has 131,331 GitHub stars, 24,452 forks, 2,464 open issues and last shipped yesterday. serviette has 58,799 stars, 1,510 forks, 8 open issues and last shipped 2 days ago. llama.cpp leads on adoption by 123% (131,331 vs 58,799 stars). llama.cpp is written in C++ under MIT; serviette is written in Python under MIT. llama.cpp has attracted 19% as many forks as stars, serviette 3%. 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 ★ 131K serviette ★ 59K category AI & Machine Learning

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Side by side

	llama.cpp	serviette
GitHub stars	★ 131K	★ 59K
License	MIT	MIT
Written in	C++	Python
Last push	2026-10-16	2026-10-15
Forks	⑂ 24K	⑂ 1.5K
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 131K 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 serviette if

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

full serviette 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 serviette

serviette is a no code, always up to date RAG server that turns a folder of documents into a chat interface and a REST API with cited answers, built for teams that want question answering over their own organisation's files without assembling or maintaining a retrieval pipeline.

read the full serviette overview →

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Frequently asked questions

Is llama.cpp or serviette more popular?

llama.cpp has 131,331 GitHub stars and serviette has 58,799. llama.cpp has the larger community by that measure.

Are llama.cpp and serviette free?

Both are open source. llama.cpp is licensed under MIT and serviette under MIT. Neither carries a licence fee.

What is the difference between llama.cpp and serviette?

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

Choose llama.cpp if you want the larger community (131,331 stars) or its MIT licence terms. Choose serviette if its feature set, stack or MIT licence fits better. Both are self-hostable.