MiniMax M3.1 Flash Gains Support from Multiple Community Quantization Schemes for Local Running
MiniMax officially summarized the various quantization and local inference schemes that the community has provided for the 428B M3.1 Flash, including UnslothAI's 2-bit Dynamic GGUF weights (143.9 GB), SAGE-EXL3, Project Maya's expert offloading, EXL3-EAQ, oQ quantization and oMLX decoding on Apple Silicon, Quark MXFP4 adapted for AMD Instinct, and an inference implementation on 4× RTX 3090 that combines quantized weights with expert caching. The official statement says that low-bit weights can reduce the memory required to load the model and the weight data moved during decoding, but they require careful calibration, efficient low-bit kernels, and expert placement across GPUs and system memory.
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The original article lists the quantization schemes and VRAM usage that multiple teams have produced for the 428B model, which can be used to gauge the threshold for local deployment.
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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.