head to head · open source

llama.cpp vs kitops

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. kitops has 1,416 stars, 185 forks, 50 open issues and last shipped 2 days ago. llama.cpp leads on adoption by 8,981% (128,581 vs 1,416 stars). llama.cpp is written in C++ under MIT; kitops is written in Go under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, kitops 13%. 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 kitops ★ 1.4K category AI & Machine Learning

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Side by side

	llama.cpp	kitops
GitHub stars	★ 129K	★ 1.4K
License	MIT	Apache-2.0
Written in	C++	Go
Last push	2026-09-17	2026-09-16
Forks	⑂ 23K	⑂ 185
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 kitops if

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

full kitops 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 kitops

KitOps is an open source DevOps tool governed by the CNCF for packaging, versioning, and securely sharing AI/ML projects. It lives in the Kubernetes AI/ML and MLOps ecosystem, and it builds on the OCI technology used by containers. A KitOps package, called a ModelKit, bundles model weights, datasets, code, and configuration into a versioned, layered artifact that can be stored in an existing container registry.

read the full kitops overview →

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Frequently asked questions

Is llama.cpp or kitops more popular?

llama.cpp has 128,581 GitHub stars and kitops has 1,416. llama.cpp has the larger community by that measure.

Are llama.cpp and kitops free?

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

What is the difference between llama.cpp and kitops?

llama.cpp is written in C++ and kitops in Go. 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 kitops?

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