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Shoehorn matches Hugging Face's most-downloaded models to your memory budget
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Shoehorn scans Hugging Face's most-downloaded models and quantizes them to fit your hardware's memory budget, then runs them locally via llama.cpp.
This tool automates the tedious task of selecting and quantizing models for local inference, giving engineers a perplexity-based quality estimate before they commit. It lowers the barrier to running large models on commodity hardware, but relies on llama.cpp and a curated model list.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
Shoehorn scans Hugging Face's most-downloaded models and ranks them by quality for your chosen memory budget.
The tool requires llama.cpp as the inference backend, which the Homebrew install pulls in automatically.
The local web app measures your machine, displays the perplexity cost of the fit, and ends at a chat button.
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