MiniMax M2.5: 230B Parameter MoE Model Coming to HuggingFace

MiniMax has officially announced the open-source release of their M2.5 model, a massive 230 billion parameter Mixture of Experts (MoE) architecture that activates only 10 billion parameters during inference. The official announcement confirms the model weights will be available on HuggingFace, making this one of the largest open-source MoE models for local deployment.

The benchmark results are particularly impressive for coding tasks, with SWE-Bench Verified achieving 80.2%, Multi-SWE-Bench at 51.3%, and BrowseComp at 76.3%. These scores put it in competition with closed-source frontier models while maintaining the efficiency benefits of MoE architecture.

For local LLM practitioners, this represents a significant opportunity to run a model with frontier-level capabilities using only 10B active parameters worth of compute, dramatically reducing memory requirements and inference costs compared to dense models of similar capability.

Source: r/LocalLLaMA · Relevance: 9/10