head to head · open source

llama.cpp vs mlop

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. mlop has 394 stars, 12 forks, 3 open issues and last shipped 8 months ago. llama.cpp leads on adoption by 33,702% (133,181 vs 394 stars). llama.cpp is written in C++ under MIT; mlop is written in Python under Apache-2.0. llama.cpp has attracted 19% 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 ★ 133K mlop ★ 394 category AI & Machine Learning

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Side by side

	llama.cpp	mlop
GitHub stars	★ 133K	★ 394
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-11-02	2026-03-05
Forks	⑂ 25K	⑂ 12
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 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 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 mlop

mlop is a Machine Learning Operations (MLOps) framework. It provides self hostable superior experimental tracking capabilities and lifecycle management for training ML models. To get started, try out our introductory notebook or get an account with us today! 🎥 Demo mlop adopts a KISS philosophy that allows it to outperform all other tools in this category. Supporting high and stable data throughput should be THE top priority for efficient MLOps. mlop logger (bottom left) v. a conventional logger (bottom right) 🚀 Getting Started Try mlop on our platform in a notebook & start integrating in just 5 lines of…

read the full mlop overview →

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Frequently asked questions

Is llama.cpp or mlop more popular?

llama.cpp has 133,181 GitHub stars and mlop has 394. 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 (133,181 stars) or its MIT licence terms. Choose mlop if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.