head to head · open source

llama.cpp vs xerj

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. xerj has 18,817 stars, 1,435 forks, 16 open issues and last shipped yesterday. llama.cpp leads on adoption by 608% (133,181 vs 18,817 stars). llama.cpp is written in C++ under MIT; xerj is written in Rust under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, xerj 8%. xerj 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 xerj ★ 19K category AI & Machine Learning

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Side by side

	llama.cpp	xerj
GitHub stars	★ 133K	★ 19K
License	MIT	Apache-2.0
Written in	C++	Rust
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 1.4K
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 xerj if

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

full xerj 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 xerj

XERJ XERJ is a community trusted local AI search that indexes any folder automatically, so your coding agent stops burning tokens reading files one by one and pulls the exact code it needs instead. Reference coding is its main use case and the clearest win: point an agent at a task and it downloads the open source repos closest to it, indexes them, and reuses how they solved the problem before writing its own code. In a controlled study that cut a coding agent's output tokens by 2.7x at the same 16/16 solve rate (case study), and people report roughly 5x in everyday work (field reports). It is enough for a…

read the full xerj overview →

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Frequently asked questions

Is llama.cpp or xerj more popular?

llama.cpp has 133,181 GitHub stars and xerj has 18,817. llama.cpp has the larger community by that measure.

Are llama.cpp and xerj free?

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

What is the difference between llama.cpp and xerj?

llama.cpp is written in C++ and xerj in Rust. 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 xerj?

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