MiniMax M3.1 Flash Now Runs Locally via Unsloth Dynamic GGUF
MiniMax announced that M3.1 Flash has joined the Unsloth community; after Unsloth dynamic GGUF quantization, its 428B MoE model's size drops to a 143.9GB 2-bit footprint. Unsloth's evaluation shows that this quantized version maintains a 91.5% top-1 token agreement rate across seven domains, can be deployed via Unsloth Desktop and llama.cpp, and offers an OpenAI-compatible API. MiniMax also noted that Unsloth has 78K+ stars on GitHub and over 500M model downloads on Hugging Face.
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The original article provides the post-quantization size and accuracy retention figures, so readers can judge how the threshold for locally self-hosting the 428B MoE model has changed.
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View original postMiniMax-M3.1-Flash can now run locally! We made 2-bit GGUF retain 91.5% top 1 accuracy while being 45% smaller. Run on 150GB RAM. MiniMax-M3.1-Flash is the strongest open model for its size & among the best overall. GGUF: https://t.co/A8Xp9uqpq1 Guide: https://t.co/gW5gp25niu https://t.co/txl6ZPRTjs
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MiniMax-M3.1-Flash can now run locally! We made 2-bit GGUF retain 91.5% top 1 accuracy while being 45% smaller. Run on 150GB RAM. MiniMax-M3.1-Flash is the strongest open model for its size & among the best overall. GGUF: https://t.co/A8Xp9uqpq1 Guide: https://t.co/gW5gp25niu https://t.co/txl6ZPRTjs
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MiniMax M3.1 Flash meets the @UnslothAI community. 🐙🦥✨
Unsloth’s Dynamic GGUFs bring our 428B MoE model to a 143.9GB 2-bit footprint, with 91.5% top-1 token agreement in their evaluation across seven domains.
That means a lower memory barrier to self-hosting, deployment through Unsloth Desktop and llama.cpp, and an OpenAI-compatible API for existing apps and agents.
With 78K+ GitHub stars and over 500M model downloads on Hugging Face, Unsloth gives M3.1 Flash a path into an established community of developers running, evaluating and building with open models.
Open weights. Local control. More ways to build.