head to head · open source

llama.cpp vs vearch

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. vearch has 2,333 stars, 366 forks, 170 open issues and last shipped 3 months ago. llama.cpp leads on adoption by 5,609% (133,181 vs 2,333 stars). llama.cpp is written in C++ under MIT; vearch is written in Python under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, vearch 16%. 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 vearch ★ 2.3K category AI & Machine Learning

← all 20902 open source comparisons

Side by side

	llama.cpp	vearch
GitHub stars	★ 133K	★ 2.3K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-11-02	2026-07-27
Forks	⑂ 25K	⑂ 366
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 vearch if

You want the vearch 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 vearch fits your workflow better

full vearch 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 vearch

简体中文 English Overview Vearch is a cloud native distributed vector database for efficient similarity search of embedding vectors in your AI applications. Key features Hybrid search : Both vector search and scalar filtering. Performance : Fast vector retrieval search from millions of objects in milliseconds. Scalability & Reliability : Replication and elastic scaling out. Document Restful APIs Tutorial 参考文档 OpenAPIs API Documentation SDK Usage Cases Use Vearch as a Memory Backend Vearch integrates with popular AI frameworks: Real world Demos VisualSearch : Vearch can be leveraged to build a complete visual search…

read the full vearch 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 serviette llama.cpp vs llama_index llama.cpp vs LocalAI llama.cpp vs faiss llama.cpp vs PageIndex llama.cpp vs Langfuse llama.cpp vs cognee llama.cpp vs taipy llama.cpp vs xerj

Frequently asked questions

Is llama.cpp or vearch more popular?

llama.cpp has 133,181 GitHub stars and vearch has 2,333. llama.cpp has the larger community by that measure.

Are llama.cpp and vearch free?

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

What is the difference between llama.cpp and vearch?

llama.cpp is written in C++ and vearch 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 vearch?

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