New Alibaba + Nanjing Univ paper claims million-token prefill can be sped up 9.36X (compared against FlashAttention-2) w…
Rohan Paul Twitter · Rohan Paul (@rohanpaul_ai) · 2026-05-24
A joint Alibaba and Nanjing University paper claims that selectively sparse attention enables 9.36X faster million-token prefill in standard LLMs compared to FlashAttention-2, requiring only lightweight adaptation.
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Extraction
Topics: sparse-attentionlong-context-inferencetransformer-efficiencyllm-optimization
Claims
- Selective sparse attention achieves a 9.36X speedup on million-token prefill compared to FlashAttention-2.
- Standard LLMs can be adapted to handle very long contexts faster without full architectural redesign.
- Full attention becomes a compute bottleneck at million-token scale, motivating selective sparsity.
Key quotes
million-token prefill can be sped up 9.36X (compared against FlashAttention-2) with only lightweight adaptation
Shows standard LLMs can handle very long context faster by making attention selectively sparse.