Block-Sparse FlashAttention Perplexity Parity in Multilingual Corpora with Llama-3 Base Models

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: Does Block-Sparse FlashAttention maintain perplexity parity with full attention on multilingual corpora better than local window attention strategies when evaluated on Llama-3 base models?

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