Cerebras has released MiniMax-M2-REAP-162B-A10B, a compressed Sparse Mixture-of-Experts (SMoE) Causal Language Model derived from MiniMax-M2, using the new Router weighted Expert Activation Pruning (REAP) method. The model keeps the behavior of the original 230B total, 10B active MiniMax M2, while pruning experts and reducing memory for deployment focused workloads such as coding agents and tool […] The post Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding Agents appeared first on MarkTechPost .