# Unsloth shows how to run Mistral's Le Chonk locally with 1-bit GGUF

> Original title: Mistral Le Chonk can now run locally!

- Source: Unsloth AI
- Published: 2026-10-29T14:27:32.000Z
- Bad Influence: https://badinfluence.news/items/cavevmyws2dwqzghd5a5tsp5s
- Original: https://x.com/UnslothAI/status/2115812784189620265

## Summary

Unsloth says its 1-bit GGUF version of Mistral Le Chonk, a 1 trillion parameter model, runs on a Mac Studio and retains 91% top-1 accuracy. The guide lists hardware requirements from 256-270GB for 1-bit to 1.2TB for 8-bit, and the smallest 1-bit quant, UD-IQ1_S, uses about 233GB of disk space.

## Why it matters

The post gives memory requirements for each quantization level, which helps readers judge whether the 1-trillion-parameter model fits their hardware.

## Post

Mistral Le Chonk can now run locally!

The 1 trillion parameter model runs on a Mac Studio with our 1-bit GGUF which retains 91% top-1 accuracy.

Le Chonk is the strongest Western open model to date.

GGUF: https://huggingface.co/unsloth/Mistral-Large-4-1T-A52B-GGUF
Guide: https://unsloth.ai/docs/models/mistral-large-4

![](https://badinfluence.news/api/img-proxy?u=https%3A%2F%2Fpbs.twimg.com%2Fmedia%2FHVzgfGCbgAA3Q5R.jpg&mode=thumb&exp=1793491200&sig=dee72b4f3d975fb5)

## Quoted post @MistralAI

> Today, we release the weights of a new and improved Le Chonk, advancing the open frontier for agentic coding and cybersecurity.
> 
> 💻 Built to build: Le Chonk ranks #1 on @cognition’s FrontierCode among open models, and sets a new benchmark for Pareto efficiency on @artificialanalysis.
> 
> 🛡 Built to defend: State-of-the-art in cyberdefense, put through the paces by the world's leading cyber vendors, including @CrowdStrike, @cloudflare, @rubrik, @zscaler, and others.
> 
> Built for sovereignty: Make it yours. Download the weights on @huggingface , customize it with Mistral Forge, and deploy it wherever you need it.
> 
> Read more from our team and customers 👇:

https://x.com/MistralAI/status/2115811242556137820