If you have llama.cpp installed, Llama uses it. Otherwise, it installs a prebuilt binary for your Mac. Models you've already installed via llama.cpp show up in the app automatically. You can install any GGUF model from Hugging Face, and Llama also recommends models that fit your Mac's hardware.
You can chat with any model in the built-in WebUI, connect other apps (coding agents, chat UIs, editors), or use the API directly. Models load when requested and unload when idle, so they don't take up memory when not in use.
Features
- 100% local — Models run on your Mac; no data ever leaves it
- Small footprint — 4 MB native macOS app
- Zero configuration — models are auto-configured with optimal settings for your Mac
- Model recommendations — a built-in list of models your Mac can run, installable in one click
- Standard storage — models live in the Hugging Face cache, shared with llama.cpp and other tools
- Built on llama.cpp — from the GGML org, developed alongside llama.cpp
If you have llama.cpp installed, Llama uses it. Otherwise, it installs a prebuilt binary for your Mac. Models you've already installed via llama.cpp show up in the app automatically. You can install any GGUF model from Hugging Face, and Llama also recommends models that fit your Mac's hardware.
You can chat with any model in the built-in WebUI, connect other apps (coding agents, chat UIs, editors), or use the API directly. Models load when requested and unload when idle, so they don't take up memory when not in use.
- 100% local — Models run on your Mac; no data ever leaves it
- Small footprint — 4 MB native macOS app
- Zero configuration — models are auto-configured with optimal settings for your Mac
- Model recommendations — a built-in list of models your Mac can run, installable in one click
- Standard storage — models live in the Hugging Face cache, shared with llama.cpp and other tools
- Built on llama.cpp — from the GGML org, developed alongside llama.cpp
To install llama.cpp , run:
To install llama-app, run:
Since this is designed for Apple hardware, is MLX supported?