head to head · open source
llama.cpp vs lightly-studio
llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. lightly-studio has 902 stars, 42 forks, 53 open issues and last shipped yesterday. llama.cpp leads on adoption by 14,665% (133,181 vs 902 stars). llama.cpp is written in C++ under MIT; lightly-studio is written in Python under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, lightly-studio 5%. 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.
← all 20902 open source comparisons
Side by side
| llama.cpp | lightly-studio | |
|---|---|---|
| GitHub stars | ★ 133K | ★ 902 |
| License | MIT | Apache-2.0 |
| Written in | C++ | Python |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 25K | ⑂ 42 |
| 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
pick lightly-studio if
- You want the lightly-studio 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 lightly-studio fits your workflow better
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 lightly-studio
Curate, Annotate, and Manage Your Data in LightlyStudio. The embedding plot shows how images relate to each other, with a preview on hover. A lasso selection filters the grid to one cluster. A search for "coffee" finds a match, and the annotation editor opens to label it. ⚡ Works smoothly with 2M+ images, embeddings included, on a single MacBook (M1, 16GB RAM). 🚀 Try it in 60 seconds LightlyStudio runs on your computer and opens in your browser. The following command downloads an example dataset with images, annotations, and evaluation results — no account needed. Local only: your images and datasets never…
read the full lightly-studio overview →
More in AI & Machine Learning
Related comparisons
More Machine Learning Infrastructure projects
Compare either of these against the rest of the Machine Learning Infrastructure field.
Frequently asked questions
Is llama.cpp or lightly-studio more popular?
llama.cpp has 133,181 GitHub stars and lightly-studio has 902. llama.cpp has the larger community by that measure.
Are llama.cpp and lightly-studio free?
Both are open source. llama.cpp is licensed under MIT and lightly-studio under Apache-2.0. Neither carries a licence fee.
What is the difference between llama.cpp and lightly-studio?
llama.cpp is written in C++ and lightly-studio 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 lightly-studio?
Choose llama.cpp if you want the larger community (133,181 stars) or its MIT licence terms. Choose lightly-studio if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.