Interleaved Local and Global Attention in Edge LLM Design

The author observes that cutting-edge small language models are increasingly using interleaved local and global attention to reduce the quadratic compute cost of transformers over long contexts. By alternating between local sliding-window attention and global attention layers, models like Gemma 4 manage long sequences efficiently without sacrificing global context. Open article →