Sparse attention can trim active HBM use without removing KV-cache capacity needs

SemiAnalysis examines how sparse-attention methods affect memory use in large-model inference. Selecting only the most relevant tokens can reduce the data processed in the attention operation, but the article notes that the full context may still need to remain available in HBM for selection. It describes HiSparse as moving KV-cache entries between accelerator HBM and host DRAM, trading some cache-miss I/O for higher throughput in long-context, high-concurrency workloads. The analysis illustrates why an algorithmic reduction in active data does not automatically eliminate system memory-capacity requirements.