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On November 2, 2026, MiniMax officially compiled a variety of quantization and local inference solutions provided by the community for the 428B M3.1 Flash, announcing that the model can run on more hardware. The solutions listed by the official include 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. MiniMax states that low-bit weights can reduce the memory required to load the model and the weight data moved during decoding, but require careful calibration, efficient low-bit kernels, and expert placement across GPU and system memory. There are currently no further developments.
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MiniMax M3.1 Flash Gains Support from Multiple Community Quantization Schemes for Local RunningDevelopments
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Nov 2
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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.