head to head · open source

llama.cpp vs ollaya

llama.cpp has 132,174 GitHub stars, 24,748 forks, 2,464 open issues and last shipped yesterday. ollaya has 1,388 stars, 83 forks, 4 open issues and last shipped yesterday. llama.cpp leads on adoption by 9,423% (132,174 vs 1,388 stars). llama.cpp is written in C++ under MIT; ollaya is written in Rust under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, ollaya 6%. 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 ★ 132K ollaya ★ 1.4K category AI & Machine Learning

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Side by side

	llama.cpp	ollaya
GitHub stars	★ 132K	★ 1.4K
License	MIT	Apache-2.0
Written in	C++	Rust
Last push	2026-10-24	2026-10-24
Forks	⑂ 25K	⑂ 83
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 132K 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 ollaya if

You want the ollaya 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 ollaya fits your workflow better

full ollaya 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…

read the full llama.cpp overview →

About ollaya

ollaya Run open decision models locally, the way Ollama runs LLMs. System 1 for AI agents: fast, typed, calibrated decisions for routing, guardrails, triage and tool approval. Website · Models · Bench · Docs · Releases · Hugging Face Created and maintained by Mert Cobanov ( @mertcobanov ). A decision model reads a state (a message, an email, a ticket, any JSON) plus typed questions ( choice , score , noul ) and returns calibrated probabilities in a single forward pass, in milliseconds. It never generates text. Ollaya pulls these models by name and serves them from a local daemon behind a self hosted, Jev…

read the full ollaya overview →

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Frequently asked questions

Is llama.cpp or ollaya more popular?

llama.cpp has 132,174 GitHub stars and ollaya has 1,388. llama.cpp has the larger community by that measure.

Are llama.cpp and ollaya free?

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

What is the difference between llama.cpp and ollaya?

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

Choose llama.cpp if you want the larger community (132,174 stars) or its MIT licence terms. Choose ollaya if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.