Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding Agents

Cerebras has introduced the MiniMax-M2-REAP-162B-A10B, a memory-efficient Sparse Mixture-of-Experts (SMoE) causal language model derived from the original MiniMax-M2, utilizing the novel Router weighted Expert Activation Pruning (REAP) technique. This approach prunes approximately 30% of experts across the model's 62 transformer layers, reducing the total parameters from 230 billion to 162 billion while maintaining the model's behavior and active parameters per token at 10 billion, optimized for deployment in coding and agentic workflows. The SM