KV Sharing, MHC, and Compressed Attention

Recent open-weight LLM releases like Gemma 4, DeepSeek V4, and others are implementing new architectural techniques—including KV sharing, compressed attention, and layer-wise attention budgeting—to reduce the computational costs and memory demands of long-context processing. As reasoning models and agent workflows keep increasing token counts, KV-cache size and attention computation have become major bottlenecks, prompting developers to adopt increasingly sophisticated efficiency optimizations within transformer blocks and attention mechanisms.