head to head · open source

llama.cpp vs VectorDBBench

llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped today. VectorDBBench has 1,178 stars, 437 forks, 184 open issues and last shipped 7 days ago. llama.cpp leads on adoption by 10,818% (128,619 vs 1,178 stars). llama.cpp is written in C++ under MIT; VectorDBBench is written in Python under MIT. llama.cpp has attracted 18% as many forks as stars, VectorDBBench 37%. 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 VectorDBBench ★ 1.2K category AI & Machine Learning

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Side by side

	llama.cpp	VectorDBBench
GitHub stars	★ 129K	★ 1.2K
License	MIT	MIT
Written in	C++	Python
Last push	2026-09-18	2026-09-11
Forks	⑂ 23K	⑂ 437
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 VectorDBBench if

You want the VectorDBBench feature set and don't need the biggest community
You prefer the MIT license terms
Your stack matches Python
You evaluated both and VectorDBBench fits your workflow better

full VectorDBBench 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 VectorDBBench

VectorDBBench(VDBBench): A Benchmark Tool for VectorDB What is VDBBench VDBBench is not just an offering of benchmark results for mainstream vector databases and cloud services, it's your go to tool for the ultimate performance and cost effectiveness comparison. Designed with ease of use in mind, VDBBench is devised to help users, even non professionals, reproduce results or test new systems, making the hunt for the optimal choice amongst a plethora of cloud services and open source vector databases a breeze. Understanding the importance of user experience, we provide an intuitive visual interface. This not only …

read the full VectorDBBench overview →

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Frequently asked questions

Is llama.cpp or VectorDBBench more popular?

llama.cpp has 128,619 GitHub stars and VectorDBBench has 1,178. llama.cpp has the larger community by that measure.

Are llama.cpp and VectorDBBench free?

Both are open source. llama.cpp is licensed under MIT and VectorDBBench under MIT. Neither carries a licence fee.

What is the difference between llama.cpp and VectorDBBench?

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

Choose llama.cpp if you want the larger community (128,619 stars) or its MIT licence terms. Choose VectorDBBench if its feature set, stack or MIT licence fits better. Both are self-hostable.