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arXiv · 2604.07815

AsyncTLS: Efficient Generative LLM Inference with Asynchronous Two-level Sparse Attention

Yuxuan Hu·Jianchao Tan·Jiaqi Zhang·Wen Zan·Pingwei Sun·Yifan Lu·Yerui Sun·Yuchen Xie·Xunliang Cai·Jing Zhang
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Abstract

Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory. While token-level sparse attention offers superior accuracy, its indexing overhead is costly; block-level methods improve efficiency but sacrifice precision. We propose AsyncTLS, a hierarchical sparse attention system that combines coarse-grained block filtering with fine-grained token selection to balance accuracy and efficiency, coupled with an asynchronous offloading engine that overlaps KV cache transfers with computation via temporal locality exploitation. Evaluated on Qwen3 and GLM-4.7-Flash across GQA, and MLA architectures, AsyncTLS achieves accuracy comparable to full attention while delivering 1.2x - 10.0x operator speedups and 1.3x - 4.7x end-to-end throughput improvements on 48k - 96k contexts.

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Yuxuan Hu, Jianchao Tan, Jiaqi Zhang, Wen Zan, Pingwei Sun, Yifan Lu, Yerui Sun, Yuchen Xie, Xunliang Cai, Jing Zhang. 2026-04-09. AsyncTLS: Efficient Generative LLM Inference with Asynchronous Two-level Sparse Attention. https://arxiv.org/abs/2604.07815

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