head to head · open source

llama.cpp vs dingo

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. dingo has 1,705 stars, 263 forks, 8 open issues and last shipped 4 days ago. llama.cpp leads on adoption by 7,711% (133,181 vs 1,705 stars). llama.cpp is written in C++ under MIT; dingo is written in Java under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, dingo 15%. 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 dingo ★ 1.7K category AI & Machine Learning

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Side by side

	llama.cpp	dingo
GitHub stars	★ 133K	★ 1.7K
License	MIT	Apache-2.0
Written in	C++	Java
Last push	2026-11-02	2026-10-30
Forks	⑂ 25K	⑂ 263
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 dingo if

You want the dingo feature set and don't need the biggest community
You prefer the Apache-2.0 license terms
Your stack matches Java
You evaluated both and dingo fits your workflow better

full dingo 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 dingo

DingoDB DingoDB is an open source distributed multi modal vector database independently designed and developed by DataCanvas, which integrates real time strong consistency, relational semantics, and vector semantics into a unified platform, DingoDB positioning itself as a distinctive multi modal database solution. With exceptional horizontal scalability and elastic scaling capabilities, it effortlessly meets enterprise grade high availability requirements. Furthermore, DingoDB offers extensive multi language interfaces and seamless compatibility with the MySQL protocol, delivering unparalleled flexibility and…

read the full dingo overview →

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Frequently asked questions

Is llama.cpp or dingo more popular?

llama.cpp has 133,181 GitHub stars and dingo has 1,705. llama.cpp has the larger community by that measure.

Are llama.cpp and dingo free?

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

What is the difference between llama.cpp and dingo?

llama.cpp is written in C++ and dingo in Java. 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 dingo?

Choose llama.cpp if you want the larger community (133,181 stars) or its MIT licence terms. Choose dingo if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.