head to head · open source

llama.cpp vs mlop

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. mlop has 392 stars, 12 forks, 3 open issues and last shipped 7 months ago. llama.cpp leads on adoption by 32,701% (128,581 vs 392 stars). llama.cpp is written in C++ under MIT; mlop is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, mlop 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 ★ 129K mlop ★ 392 category AI & Machine Learning

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Side by side

	llama.cpp	mlop
GitHub stars	★ 129K	★ 392
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-17	2026-03-05
Forks	⑂ 23K	⑂ 12
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 mlop if

You want the mlop 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 mlop fits your workflow better

full mlop 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 mlop

mlop is an Apache 2.0 licensed Python experiment tracking and lifecycle management framework for machine learning teams, offered both as a hosted service at app.mlop.ai and as a self hosted server run with docker compose.

read the full mlop overview →

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Frequently asked questions

Is llama.cpp or mlop more popular?

llama.cpp has 128,581 GitHub stars and mlop has 392. llama.cpp has the larger community by that measure.

Are llama.cpp and mlop free?

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

What is the difference between llama.cpp and mlop?

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

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