# MiniMax thanks community builders for quantizing M3.1 Flash to run locally

> Original title: MiniMax M3.1 Flash, on more of your hardware.

- Source: MiniMax
- Published: 2026-11-01T20:49:42.000Z
- Bad Influence: https://badinfluence.news/items/f1kamjncgbnbulku78rjukdn5
- Original: https://x.com/MiniMax_AI/status/2116996123164569606

## Summary

MiniMax thanks community builders for quantizing its 428B M3.1 Flash model so it can run on more local hardware. The post credits Unsloth with 2-bit Dynamic GGUF weights at 143.9 GB, and names other contributors for low-bit kernels, expert offloading, and Apple Silicon and AMD ports. MiniMax says lower-bit weights cut the memory needed to load the model and the weight data moved during decoding.

## Post

MiniMax M3.1 Flash, on more of your hardware.

Quantizing a 428B model opens up more ways to run it locally. Lower-bit weights reduce the memory needed to load the model and the weight data moved during decoding. Making those savings useful takes careful calibration, efficient low-bit kernels, and hardware-aware placement of experts across GPU and system RAM.

Thank you to the builders doing that work:

• @UnslothAI @danielhanchen — 2-bit Dynamic GGUF weights at 143.9 GB, with calibration and fidelity testing across coding, tool use and other workloads.
• @ViC305 — SAGE-EXL3, token-level fidelity measurements and a single-DGX-Spark recipe.
• @PeasantSmith — Project Maya quants and expert offloading for local coding and tool use.
• @MiaAI_lab — EXL3-EAQ quantization work.
• @jundotkim + mlx-community — oQ quants and DSpark-powered decoding through oMLX on Apple Silicon.
• @AIatAMD — Quark MXFP4 adapted for AMD Instinct.
• @atomic_chat_hq — GGUFs for local desktop workflows.
• @superalesha — custom inference work combining quantized weights, expert caching and system RAM on 4× RTX 3090.
Every shared quant, kernel, measurement and recipe gives the next builder a better starting point. Thank you.

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