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

• URL: http://arxiv.org/abs/2609.00097v1
• Date: Mon, 31 Aug 2026 15:13:20 GMT
• Title: Faster Than Flash: Exploiting Attention Sparsity for Efficient Long-Context Decoding
• Abstract summary: We present Faster Flash Decoding (FFD), a novel hardware-algorithm co-design framework designed to break the memory wall in long-context decoding.<n>FFD integrates the selector and computer into a fully fused kernel, replacing external metadata indices with content-aware scanning.<n>It achieves up to 11.6x kernel-level speedup and scaling to 256K context length, with 2.37x end-to-end throughput improvement.
• Score: 51.48425605758328
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/qluoluo/faster-flash-decoding

Related papers

• LLM Inference in a Flash! [66.43896490006965]
  Large Language Models (LLMs) have shown impressive capabilities across a range of natural language processing tasks.<n>We present an end-to-end integer-only quantization approach to eliminate expensive floating-point computations.
  arXiv  Detail & Related papers  (2026-09-14T18:04:30Z)
• FluxBin: Flexible LUT-based Ultra-low-bit LLM Inference by Algorithm-Kernel Synergy [32.21687694159134]
  FluxBin is an algorithm that synerizes binary quantization with a highly optimized kernel.<n>FluxBin achieves up to $5.92times speedup and $10.19times energy savings.<n>This effectively enables the deployment of 70B-scale models on one single A100 bases with a $4times memory reduction.
  arXiv  Detail & Related papers  (2026-08-16T08:01:06Z)
• You Only Index Once: Cross-Layer Sparse Attention with Shared Routing [61.29627714699688]
  Cross-layer sparse attention (A) is built on top of KV-sharing architectures such as YOCO.<n>A single indexer computes token-level top-k selection once and reuses the resulting index across layers.<n>Results show CLSA is both accurate and efficient, achieving up to 7.6x decoding speedup and 17.1x overall throughput improvement at 128K context.
  arXiv  Detail & Related papers  (2026-06-04T17:54:04Z)
• Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference [36.81958522560282]
  We introduce Flux Attention, a context-aware framework that dynamically optimize attention at the layer level.<n>As a parameter-efficient approach, our framework requires only 12 hours of training on 8$times$A800 GPUs.<n>Our framework achieves a superior trade-off between performance and inference speed compared with baseline models, with speed improvements of up to $2.8times$ and $2.0times$ in the prefill and decode stages.
  arXiv  Detail & Related papers  (2026-04-08T07:36:17Z)
• Towards Practical Lossless Neural Compression for LiDAR Point Clouds [84.36825469211375]
  extreme sparsity of high-precision geometric details hinders efficient context modeling.<n>Our framework comprises two lightweight modules.<n>Experiments demonstrate competitive compression performance at real-time speed.
  arXiv  Detail & Related papers  (2026-03-26T10:02:07Z)
• Fast Forward: Accelerating LLM Prefill with Predictive FFN Sparsity [5.223181756178433]
  We introduce FastForward, a predictive sparsity framework that accelerates large language model (LLM) prefill through block-wise, context-aware FFN sparsity.<n>FastForward delivers up to 1.45$times$ compute-bound speedup at 50% FFN sparsity with $$ 6% accuracy loss compared to the dense baseline on LongBench.
  arXiv  Detail & Related papers  (2026-01-30T23:23:40Z)
• Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction [72.27673320976933]
  Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and parallel decoding.<n>Current caching techniques accelerate decoding by storing full-layer states, yet impose substantial memory usage.<n>We propose Sparse-dLLM, the first training-free framework integrating dynamic cache eviction with sparse attention.
  arXiv  Detail & Related papers  (2025-08-04T16:14:03Z)
• RetroInfer: A Vector-Storage Approach for Scalable Long-Context LLM Inference [27.69137902678418]
  RetroInfer is a novel system that exploits the inherent attention sparsity to accelerate long-context inference.<n>We show up to 4.5X speedup over full attention within GPU memory limits and up to 10.5X over sparse attention baselines when KV cache is extended to CPU memory.
  arXiv  Detail & Related papers  (2025-05-05T18:01:17Z)
• UIO-LLMs: Unbiased Incremental Optimization for Long-Context LLMs [111.05657299071648]
  UIO-LLMs is an incremental optimization approach for memory-enhanced transformers under long-context settings.<n>We refine the training process using the Truncated Backpropagation Through Time (TBPTT) algorithm.<n>UIO-LLMs successfully handle long context, such as extending the context window of Llama2-7b-chat from 4K to 100K tokens with minimal 2% additional parameters.
  arXiv  Detail & Related papers  (2024-06-26T08:44:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.