Full-Pipeline Inference Optimization for MiMo-V2.5 Series: Pushing Hybrid SWA Efficiency to the Limit

The problem is that Hybrid Sliding Window Attention (SWA) reduces compute and KVCache storage compared to Full Attention, but realizing these gains in production requires substantial engineering effort. The method systematically optimizes the KVCache system with layerwise prefetch, SWA-aware prefix cache trees, and specialized placement strategies, and builds GCache, a high-performance distributed cache infrastructure with RDMA-optimized networking. Experimental evidence shows strict O(W) SWA storage and high cache hit rates are achieved, with the system being the first large-scale LLM serving system in production to efficiently cover the Hybrid SWA + MoE + multimodal composite architecture. This matters because it pushes hybrid SWA efficiency to the limit, enabling practical deployment of complex multimodal models with reduced computation and storage overhead.