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Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Yuan et al., 2025, Native Sparse Attention (Proceedings of the 63rd Annual Meeting of ACL (Volume 1: Long Papers))

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4 Efficiency Analysis

  1. What speedups did NSA achieve at 64k context?
    Compared with Triton FlashAttention-2 on A100s: up to 9.0× in the forward pass and 6.0× in the backward pass during training, and up to 11.6× in decoding, where speed tracks how much KV cache must be read. Speedups grow with context length.
    • Yuan et al., 2025, Native Sparse Attention (Proceedings of the 63rd Annual Meeting of ACL (Volume 1: Long Papers)), §4, p. 23085

Source

  • Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang and Wangding Zeng. 2025. Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 23078–23097. License: CC BY 4.0. Flashcards adapted from this work. doi:10.18653/v1/2025.acl-long.1126 https://aclanthology.org/2025.acl-long.1126/