head to head · open source

llama.cpp vs lancedb

llama.cpp has 128,880 GitHub stars, 23,468 forks, 2,464 open issues and last shipped today. lancedb has 11,473 stars, 1,060 forks, 627 open issues and last shipped yesterday. llama.cpp leads on adoption by 1,023% (128,880 vs 11,473 stars). llama.cpp is written in C++ under MIT; lancedb is written in Rust under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, lancedb 9%. 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 lancedb ★ 11K category AI & Machine Learning

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Side by side

	llama.cpp	lancedb
GitHub stars	★ 129K	★ 11K
License	MIT	Apache-2.0
Written in	C++	Rust
Last push	2026-09-20	2026-09-19
Forks	⑂ 23K	⑂ 1.1K
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 lancedb if

You want the lancedb 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 lancedb fits your workflow better

full lancedb 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 lancedb

LanceDB is an open source, developer friendly embedded retrieval library for multimodal AI, maintained in Rust and released under the Apache 2.0 license. It describes itself as a multimodal AI lakehouse: a single place where developers can build, train and analyze AI workloads over vectors, metadata and multimodal data such as text, images, videos and point clouds. It is built on top of the Lance columnar format, which provides the storage layer for efficient storage and analytics.

read the full lancedb overview →

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Frequently asked questions

Is llama.cpp or lancedb more popular?

llama.cpp has 128,880 GitHub stars and lancedb has 11,473. llama.cpp has the larger community by that measure.

Are llama.cpp and lancedb free?

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

What is the difference between llama.cpp and lancedb?

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

Choose llama.cpp if you want the larger community (128,880 stars) or its MIT licence terms. Choose lancedb if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.