head to head · open source

llama.cpp vs Laminar

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. Laminar has 3,265 stars, 239 forks, 116 open issues and last shipped yesterday. llama.cpp leads on adoption by 3,838% (128,581 vs 3,265 stars). llama.cpp is written in C++ under MIT; Laminar is written in TypeScript under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, Laminar 7%. Laminar 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 Laminar ★ 3.3K category AI & Machine Learning

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Side by side

	llama.cpp	Laminar
GitHub stars	★ 129K	★ 3.3K
License	MIT	Apache-2.0
Written in	C++	TypeScript
Last push	2026-09-17	2026-09-17
Forks	⑂ 23K	⑂ 239
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 Laminar if

You want the Laminar feature set and don't need the biggest community
You prefer the Apache-2.0 license terms
Your stack matches TypeScript
You evaluated both and Laminar fits your workflow better

full Laminar 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 Laminar

Laminar is an open source observability platform purpose built for AI agents, distributed under the Apache 2.0 license and written primarily in TypeScript. It was built by the team behind Y Combinator's S24 batch and lives in the AI and machine learning infrastructure ecosystem, with a topic list spanning agent observability, LLM evaluation, LLMOps and AIOps. The project ships as a tracing and evaluation stack rather than a general purpose APM tool, and its homepage at laminar.sh hosts both documentation and a managed offering.

read the full Laminar overview →

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Frequently asked questions

Is llama.cpp or Laminar more popular?

llama.cpp has 128,581 GitHub stars and Laminar has 3,265. llama.cpp has the larger community by that measure.

Are llama.cpp and Laminar free?

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

What is the difference between llama.cpp and Laminar?

llama.cpp is written in C++ and Laminar in TypeScript. 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 Laminar?

Choose llama.cpp if you want the larger community (128,581 stars) or its MIT licence terms. Choose Laminar if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.