● ModelsOct 30, 2026 · 02:358 sources
Unsloth Shrinks MiniMax's 428B M3.1 Flash Model to Run Locally on 150GB RAM
From marketbrief.now
AI;DR
Unsloth released 2-bit dynamic GGUF quantizations of MiniMax's 428B parameter M3.1 Flash model that reduce its footprint by 45% to 143.9GB and allow it to run locally on 150GB of RAM, retaining 91.5% top 1 token accuracy across seven domains. Independent evaluations showed lower coding benchmark results than MiniMax reported, with two outside tests recording a score of 26 on Terminal-Bench 4.0 compared to MiniMax's claimed 38. The local quantizations follow MiniMax's October 23 release of open weights for M3.1 Flash, a mixture-of-experts model that activates 23B parameters per token.
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- SUPPORT@valsai“The biggest improvement came on Terminal-Bench 4. M3 only solved 1% of tasks, while M3.1-Flash reached 25.2%”x.com
- SUPPORT@teortaxestex“they actually rebranded M3 as M3.1 Flash, just sparsified the attn in 3 more layers, quantized it and added DSpark on top”x.com
- SUPPORT@valsai“On Tax Agent and Legal Research, it generated roughly 15 times as many output tokens as M3, most of them devoted to reasoning.”x.com
- SUPPORT@andrew_n_carr“I cannot believe a model this size, this good, is open source”x.com
- SUPPORT@danielhanchen“By default, MiniMax-M3.1-Flash uses NVFP4 for MoE layers and BF16 / MXFP8 for the rest. UD-IQ1_M retains ~89.9% top-1%.”x.com