Block-Sparse FlashAttention vs Standard Sparse Attention in Llama-3 at 128K Tokens: Accuracy and Efficiency Trade-offs

Modern large language models increasingly require long contexts for reasoning and multi-document tasks, but attention's quadratic complexity creates a severe computational bottleneck. We present Block-Sparse FlashAttention (BSFA), a drop-in replacement that accelerates long-context inference while preserving model quality. Unlike methods that predict importance before computing scores, BSFA computes exact query-key similarities to select the top-k most important value blocks for each query. By comparing per-block maximum scores against calibrated thresholds, we skip approximately 50\% of the co

Research goal: How does the needle-in-a-haystack retrieval accuracy of Block-Sparse FlashAttention compare to standard sparse attention mechanisms in Llama-3 when scaled to 128K tokens, and what is the trade-off between accuracy and computational efficiency?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.