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

• URL: http://arxiv.org/abs/2604.07815v1
• Date: Thu, 09 Apr 2026 05:15:16 GMT
• Title: AsyncTLS: Efficient Generative LLM Inference with Asynchronous Two-level Sparse Attention
• Abstract summary: AsyncTLS is a hierarchical sparse attention system that combines coarse-grained block filtering with fine-grained token selection.<n>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.
• Score: 32.26860037633772
• License: http://creativecommons.org/licenses/by/4.0/
• 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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