Show HN: Shoehorn – Quantize any model down to run on your machine(notactuallytreyanastasio.github.io)
67 pointsby rhgraysoniiAug 18, 2026

7 Comments

mbuchel-hn•Aug 18, 2026
does this work similar to airllm? i am wondering how it would handle something like quantizing kimi k3 on a budget of 8 gbs, or is that something you are not attempting to solve yet?
rhgraysonii•Aug 18, 2026
Yes that is exactly what this does.
kennywinker•Aug 19, 2026
Could you explain what happens when you try to shoehorn a 2.4T parameter model into a 24gb m4 mac?
akshay_akula•Aug 19, 2026
Wondering the same thing but for 48gb M5 Max.
metalliqaz•Aug 21, 2026
extreme divergence would be my guess
hmokiguess•Aug 18, 2026
Reminds me of https://github.com/AlexsJones/llmfit
rhgraysonii•Aug 18, 2026
LLMFit tells you what can run on something. I built something quite similar to their search into Shoehorn now.
jaylane•Aug 18, 2026
tried it out but based on the model sizing result i got i got an insufficient memory error when the server started running
rhgraysonii•Aug 18, 2026
If you could post an issue if you still have the error around that would be awesome.
akshay_akula•Aug 19, 2026
This is interesting. I wonder how it could work with something like https://github.com/JustVugg/colibri.
jedbrooke•Aug 21, 2026
I gotta laugh at some of the models it suggests, for example:

> AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF

you’re telling me you managed to fit Fable 5 into just 4B?

chompychop•Aug 21, 2026
I gotta laugh at your thought process: knowing Fable 5 is a large frontier model, you're telling me that the first thing that came to your mind on seeing that model name is that it's a quantized version of Fable? As opposed to a distillation/fine-tuning on Fable responses?
unrented7977•Aug 21, 2026
Don't make fun of people you think are ignorant, it's a pretty shitty look
chompychop•Aug 21, 2026
Well, then don't get all snarky and dismissive of things you might not be knowledgeable about ("you" here referring to OP).
metalliqaz•Aug 21, 2026
Well to be fair here... the title of this post doesn't mention fine tuning, it mentions quantization.
sscarduzio•Aug 21, 2026
The project name is perfect!
puttycat•Aug 21, 2026
This is really impressive. Can you say a bit about the underlying process? I'm guessing this is post-training qantization? Isn't PTQ also resource-intensive? (Ie might not work on any machine)