Local LLM
llmfit
Hundreds of models & providers. One command to find what runs on your hardware.
★ 37.5k starsMIT
View guide →Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level
node-llama-cpp is an open-source local llm project by withcatai (withcatai/node-llama-cpp on GitHub). It has 2,187 GitHub stars and 221 forks, is released under the MIT license, and was last updated on GitHub on 2 October 2026.
One binary or app gets you a working local model setup.
Pick a size that fits your RAM/VRAM; smaller models run on laptops.
Use the built-in UI, or point OpenAI-compatible clients at it.
Airplane mode works — your prompts never leave the device.
RAG, tools, and agents build on the same base.
Smart compression runs big models on modest hardware.
Works on planes, in basements, behind firewalls.
Swap between hundreds of open models freely.
Existing tools plug in by changing one URL.
Ask questions over your PDFs, notes, and codebases.
Download the source and move into the project folder.
git clone https://github.com/withcatai/node-llama-cpp.git
cd node-llama-cppEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandIt is an open-source project at withcatai/node-llama-cpp with 221 forks. Check the repo's contributors and recent commits to see how active maintenance is.
MIT licensed — use it commercially, no strings attached.
8B models run on 16GB RAM; bigger models want a real GPU.
A recent 8–32B instruct model is the sweet spot for most machines.
Yes — with no network calls, there's nothing to leak.
Hundreds of models & providers. One command to find what runs on your hardware.
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Free and open — grab the source, star the repo, and start creating.