MiniMax M3.1 Flash 获多个社区量化方案支持本地运行
MiniMax M3.1 Flash, on more of your hardware.
MiniMax 官方汇总了社区为 428B 的 M3.1 Flash 提供的多种量化与本地推理方案，包括 UnslothAI 的 2-bit Dynamic GGUF 权重（143.9 GB）、SAGE-EXL3、Project Maya 的专家卸载、EXL3-EAQ、Apple Silicon 上的 oQ 量化与 oMLX 解码、适配 AMD Instinct 的 Quark MXFP4，以及在 4× RTX 3090 上结合量化权重与专家缓存的推理实现。官方称低位权重可降低加载模型所需内存和解码时搬运的权重数据，但需要仔细校准、高效低位 kernel 以及跨 GPU 与系统内存的专家放置。
正文
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.