How sparse attention solves the memory bottleneck in long-context LLMs

Sparse attention techniques address the key-value cache bottleneck that slows long-context LLM inference. The KV cache grows linearly with generated tokens, sits in GPU VRAM, and reading it from high-bandwidth memory dominates runtime. Sparse attention optimizes memory while preserving accuracy for coding assistants and research agents.