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.