MiniMax M2.7 Launches on OpenRouter

🔑 Enhanced Key Takeaways

• •MiniMax M2.7 features a Mixture-of-Experts (MoE) architecture with 230 billion total parameters and 10 billion active parameters using a top-2 routing strategy across 8 experts.[1][9]
• •The model participated in 22 machine learning competitions on MLE Bench Lite, covering full ML workflows in low-resource settings on a single A30 GPU.[3]
• •M2.7 achieves 76.5% on SWE Multilingual and 52.7% on Multi SWE Bench, with 97% skill adherence on over 40 complex skills exceeding 2000 tokens.[3][4]

🛠️ Technical Deep Dive

• •MoE architecture: 230B total parameters, 10B active per token, 8 experts with top-2 routing.[1][9]
• •Core specs: 32 layers, hidden dimension 4096, 32 attention heads, 8 KV heads, SwiGLU activation, RMSNorm.[1]
• •Position embeddings: Rotary Position Embeddings (RoPE) for long-context stability; supports up to 128k native context, extended to 204k on OpenRouter.[1]
• •Inference: VRAM ~460GB FP16 to ~115-130GB 4-bit; optimized for vLLM with GPU clusters like 4x H100 for FP8.[1][8]
• •Agent features: Native tool integration, structured reasoning traces for error recovery, internal decision logs.[1]

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline