head to head · open source

llama.cpp vs vearch

llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped today. vearch has 2,326 stars, 365 forks, 170 open issues and last shipped 2 months ago. llama.cpp leads on adoption by 5,430% (128,619 vs 2,326 stars). llama.cpp is written in C++ under MIT; vearch is written in Python under Apache-2.0. llama.cpp has attracted 18% 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 ★ 129K vearch ★ 2.3K category AI & Machine Learning

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Side by side

	llama.cpp	vearch
GitHub stars	★ 129K	★ 2.3K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-18	2026-07-27
Forks	⑂ 23K	⑂ 365
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 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 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 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 s…

read the full vearch overview →

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Frequently asked questions

Is llama.cpp or vearch more popular?

llama.cpp has 128,619 GitHub stars and vearch has 2,326. 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 (128,619 stars) or its MIT licence terms. Choose vearch if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.