Local LLM
llama.cpp
LLM inference in C/C++
★ 130.2k starsMIT
View guide →Quantization, kernels, runtime and inference engine for mobiles, wearables, smart home and robots
Cactus is an open-source local llm project by cactus-compute (cactus-compute/cactus on GitHub). It has 6,090 GitHub stars and 512 forks, is released under the Custom (check repo) license, and was last updated on GitHub on 26 September 2026.
One-command installers exist for every major OS; servers and engines use Docker or simple binaries.
Pull from a built-in library or point at any GGUF/compatible weights you already have.
Use the bundled chat UI, or point your apps and coding agents at the local OpenAI-style endpoint.
Pick model sizes that fit your RAM/VRAM; quantization options trade a little quality for a lot of speed.
Prompts never leave your machine — no accounts, no logging, no training on your data.
Once weights are downloaded, everything runs with the network cable unplugged.
Local endpoints your existing tools and code can call with minimal changes.
Llama, Qwen, Gemma, DeepSeek, Mistral and more, in sizes from phone-friendly to workstation-class.
Coding assistants and automation tools plug straight into the local server.
Run bigger models on smaller hardware via GGUF and friends.
Clone the repository or download the latest release for your OS.
git clone https://github.com/cactus-compute/cactus.gitEvery project documents its own setup — the README's install section is the source of truth.
Start the app or server as documented and open the interface it provides.
Roughly the model size in GB plus headroom — 8B models fit comfortably on 16GB machines with quantization.
Local wins on privacy and cost; cloud wins on raw frontier quality. Many people use both.
It is an open-source project at cactus-compute/cactus with 512 forks. Check the repo's contributors and recent commits to see how active maintenance is.
A recent mid-size open model in the library's recommendations — start small and scale up.
Yes — the local API plus client libraries make it a popular backend for indie apps.
Yes — several of these are optimized for it, and unified memory helps a lot.
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Free and open — grab the source, star the repo, and start creating.