Faster Than Flash: Exploiting Attention Sparsity for Efficient Long-Context Decoding

Abstract:The development of long-context Large Language Models (LLMs) is constrained by the memory bandwidth bottleneck and quadratic complexity of the attention mechanism during decoding. To overcome the inherent trade-offs between the memory overhead of metadata-based metrics and the computational inefficiency of adaptive selection strategies, we present Faster Flash Decoding (FFD), a novel hardware-algorithm co-design framework designed to break the memory wall in long-context decoding. FFD integrates the selector and computer into a fully fused kernel, replacing external metadata indices with content-aware scanning via low-bit quantization. Furthermore, we introduce the top-delta strategy, which dynamically filters blocks to achieve distribution-adaptive sparsity without global synchronization. Offering a training-free and plug-and-play solution, FFD also enables the reuse of scanning results for computation, achieving up to 11.6x kernel-level speedup and scaling to 256K context length, with 2.37x end-to-end throughput improvement. Empirical validation on RULER and LongBench confirms that FFD maintains model accuracy while delivering high-ratio sparsity, with code available at this https URL

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.00097 [cs.LG]

(or arXiv:2609.00097v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.00097 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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