head to head · open source

llama.cpp vs examples

llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped today. examples has 3,043 stars, 1,073 forks, 64 open issues and last shipped 14 days ago. llama.cpp leads on adoption by 4,127% (128,619 vs 3,043 stars). llama.cpp is written in C++ under MIT; examples is written in Jupyter Notebook under MIT. llama.cpp has attracted 18% as many forks as stars, examples 35%. 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 examples ★ 3.0K category AI & Machine Learning

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Side by side

	llama.cpp	examples
GitHub stars	★ 129K	★ 3.0K
License	MIT	MIT
Written in	C++	Jupyter Notebook
Last push	2026-09-18	2026-09-04
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 examples if

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

full examples 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 examples

Pinecone Examples is an MIT licensed collection of Jupyter Notebooks and sample applications that lets developers get hands on with Pinecone vector databases and common AI patterns, tools, and algorithms.

read the full examples overview →

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Frequently asked questions

Is llama.cpp or examples more popular?

llama.cpp has 128,619 GitHub stars and examples has 3,043. llama.cpp has the larger community by that measure.

Are llama.cpp and examples free?

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

What is the difference between llama.cpp and examples?

llama.cpp is written in C++ and examples in Jupyter Notebook. 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 examples?

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