head to head · open source

llama.cpp vs xerj

llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped today. xerj has 1,913 stars, 238 forks, 16 open issues and last shipped yesterday. llama.cpp leads on adoption by 6,623% (128,619 vs 1,913 stars). llama.cpp is written in C++ under MIT; xerj is written in Rust under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, xerj 12%. 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 xerj ★ 1.9K category AI & Machine Learning

← all 8884 open source comparisons

Side by side

	llama.cpp	xerj
GitHub stars	★ 129K	★ 1.9K
License	MIT	Apache-2.0
Written in	C++	Rust
Last push	2026-09-18	2026-09-17
Forks	⑂ 23K	⑂ 238
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 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 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 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 smalle…

read the full xerj 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.

llama.cpp vs Ollama llama.cpp vs vllm llama.cpp vs GPT4All llama.cpp vs llama_index llama.cpp vs LocalAI llama.cpp vs PageIndex llama.cpp vs Langfuse llama.cpp vs cognee llama.cpp vs taipy llama.cpp vs dagster llama.cpp vs zvec llama.cpp vs langchain4j

Frequently asked questions

Is llama.cpp or xerj more popular?

llama.cpp has 128,619 GitHub stars and xerj has 1,913. 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 (128,619 stars) or its MIT licence terms. Choose xerj if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.