Shoehorn – Quantize any model down to run on your machine
Working on Mac, Linux, and Windows now. I include a simple GUI to find new models and get things built and set up. It is working quite well across a few models for me. The GitHub README and DESIGN.md files go into detail of the how/why and it's working remarkably well so far. https://github.com/notactuallytreyanastasio/shoehorn
In plain words
Shoehorn is a tool that quantizes large language models to fit precisely within a user's available VRAM, maximizing quality by using nearly all available memory. It works on Mac, Linux, and Windows, featuring a GUI to browse compatible models from Hugging Face and manage the quantization process. The tool uses mathematical optimization to assign mixed-precision quantization per tensor, routinely utilizing over 99.99% of the specified memory budget while running models locally via llama.cpp.
written from the facts on this page · September 2026
From the sources
Quantize a BF16 GGUF with an imatrix to exactly fit your VRAM, then run it with llama.cpp. Every spare megabyte spent where it buys the most quality.
Preset 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. 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. shoehorn needs llama.cpp on your PATH as the inference backend (the…from notactuallytreyanastasio.github.io
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