head to head · open source

llama.cpp vs examples

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. examples has 3,051 stars, 1,062 forks, 64 open issues and last shipped 11 days ago. llama.cpp leads on adoption by 4,265% (133,181 vs 3,051 stars). llama.cpp is written in C++ under MIT; examples is written in Jupyter Notebook under MIT. llama.cpp has attracted 19% 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 ★ 133K examples ★ 3.1K category AI & Machine Learning

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Side by side

	llama.cpp	examples
GitHub stars	★ 133K	★ 3.1K
License	MIT	MIT
Written in	C++	Jupyter Notebook
Last push	2026-11-02	2026-10-23
Forks	⑂ 25K	⑂ 1.1K
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 133K 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 LLM inference in C/C++ ggml / ops / maintainer PRs%20sort%3Aupdated desc) / dev stats / lib llama API / llama server REST API Quick start A few options to get llama.cpp installed on your machine: Visit and follow the instructions Run with Docker see our Docker documentation Download pre built binaries from the releases page Build from source by cloning this repository check out our build guide Once installed: VLM session with llama cli Built in web UI against llama serve Description The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state of the art performance on a…

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About examples

Pinecone Examples This repository is a collection of sample applications and Jupyter Notebooks that you can run, download, study and modify in order to get hands on with Pinecone vector databases and common AI patterns, tools and algorithms. Two kinds of examples This repo contains: 1. Production ready examples in ./docs that receive regular review and support from the Pinecone engineering team 2. Examples optimized for learning and exploration of AI techniques in ./learn and patterns for building different kinds of applications, created and maintained by the Pinecone Developer Advocacy team. We appreciate your…

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Frequently asked questions

Is llama.cpp or examples more popular?

llama.cpp has 133,181 GitHub stars and examples has 3,051. 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 (133,181 stars) or its MIT licence terms. Choose examples if its feature set, stack or MIT licence fits better. Both are self-hostable.