GoKawiilPreset quantizations ignore your hardware: pick one that fits and you either waste hundreds of megabytes of quality headroom or find out at load time it didn't fit after all. shoehorn starts from the memory you actually have, subtracts what inference itself needs, and solves a per-tensor mixed-precision assignment that lands within a rounding error of the remainder — routinely using 99.99% of the budget, sometimes to the byte.
Make any language model fit the memory you actually have.
Conversation room 4k tokens — short chats 8k tokens — everyday use 16k tokens — long documents 32k tokens — the whole novella
Pick your hardware and this page scans Hugging Face's most-downloaded models for ones shoehorn can fit to your budget — ranked by the quality your memory affords. Runs entirely in your browser.
Get shoehorn
Install
shoehorn needs llama.cpp on your PATH as the inference backend (the Homebrew install pulls it in for you). Then shoehorn ui opens the local app — pick a model, press one button, chat.
brew install notactuallytreyanastasio/shoehorn/shoehorn Copy