# TidalDecode: Fast and Accurate LLM Decoding with Position Persistent Sparse Attention

arXiv: https://arxiv.org/abs/2410.05076 · HTML: https://mlsanity.com/paper/2410.05076/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019)
- [H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models](https://mlsanity.com/paper/2306.14048/) (2023)
- [Compressive Transformers for Long-Range Sequence Modelling](https://mlsanity.com/paper/1911.05507/) (2019)
- [Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference](https://mlsanity.com/paper/2406.10774/) (2024)
- [Efficient Attentions for Long Document Summarization](https://mlsanity.com/paper/2104.02112/) (2021)
- [Beyond the Limits: A Survey of Techniques to Extend the Context Length in Large Language Models](https://mlsanity.com/paper/2402.02244/) (2024)
- [Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction](https://mlsanity.com/paper/2409.17422/) (2024)

## Built on by (most cited first, 29 of 29)

- [Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity](https://mlsanity.com/paper/2502.01776/) (2025)
- [Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification](https://mlsanity.com/paper/2412.00876/) (2024)
- [Twilight: Adaptive Attention Sparsity with Hierarchical Top-p Pruning](https://mlsanity.com/paper/2502.02770/) (2025)
- [IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse](https://mlsanity.com/paper/2603.12201/) (2026)
- [Empowering GraphRAG with Knowledge Filtering and Integration](https://mlsanity.com/paper/2503.13804/) (2025)
- [SlimInfer: Accelerating Long-Context LLM Inference via Dynamic Token Pruning](https://mlsanity.com/paper/2508.06447/) (2025)
- [BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding](https://mlsanity.com/paper/2512.12087/) (2025)
- [Kascade: A Practical Sparse Attention Method for Long-Context LLM Inference](https://mlsanity.com/paper/2512.16391/) (2025)
- [HySparse: A Hybrid Sparse Attention Architecture with Oracle Token Selection and KV Cache Sharing](https://mlsanity.com/paper/2602.03560/) (2026)
- [CommonKV: Compressing KV Cache with Cross-layer Parameter Sharing](https://mlsanity.com/paper/2508.16134/) (2025)
- [Value-Aware Stochastic KV Cache Eviction for Reasoning Models](https://mlsanity.com/paper/2606.03928/) (2026)
- [DELTA: Dynamic Layer-Aware Token Attention for Efficient Long-Context Reasoning](https://mlsanity.com/paper/2510.09883/) (2025)
- [LycheeDecode: Accelerating Long-Context LLM Inference via Hybrid-Head Sparse Decoding](https://mlsanity.com/paper/2602.04541/) (2026)
- [QUOKA: Query-Oriented KV Selection For Efficient LLM Prefill](https://mlsanity.com/paper/2602.08722/) (2026)
- [You Only Index Once: Cross-Layer Sparse Attention with Shared Routing](https://mlsanity.com/paper/2606.06467/) (2026)
- [LiteTopK: Exploiting the Curse of Dimensionality for a Fused Indexer-TopK Kernel in Long-Context Sparse Attention](https://mlsanity.com/paper/2607.11976/) (2026)
- [LongCat Sparse Attention: Taming the Lightning via Streaming-aware Hierarchical Cross-Layer Indexing](https://mlsanity.com/paper/2608.01662/) (2026)
- [Fast Forward: Accelerating LLM Prefill with Predictive FFN Sparsity](https://mlsanity.com/paper/2602.00397/) (2026)
- [Slow-Fast Inference: Training-Free Inference Acceleration via Within-Sentence Support Stability](https://mlsanity.com/paper/2603.12038/) (2026)
- [MAC-Attention: a Match-Amend-Complete Scheme for Fast and Accurate Attention Computation](https://mlsanity.com/paper/2604.00235/) (2026)
- [STS: Efficient Sparse Attention with Speculative Token Sparsity](https://mlsanity.com/paper/2605.15508/) (2026)
- [Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents](https://mlsanity.com/paper/2606.06453/) (2026)
- [LOCKS: Page-Local Compact Key Summaries for Efficient Long-Context Decoding](https://mlsanity.com/paper/2607.24555/) (2026)
- [Language Models Can Control Their Own Attention](https://mlsanity.com/paper/2609.02737/) (2026)
- [ASPIRE: Asynchronous Batched Self-Speculative Decoding for Long-Context LLM Inference](https://mlsanity.com/paper/2609.17943/) (2026)
- [A Faster Generalized Two-Stage Approximate Top-K](https://mlsanity.com/paper/2506.04165/) (2025)
- [HyLRA: Hybrid Layer Reuse Attention for Efficient Long-Context Inference](https://mlsanity.com/paper/2602.00777/) (2026)
- [Salca: A Sparsity-Aware Hardware Accelerator for Efficient Long-Context Attention Decoding](https://mlsanity.com/paper/2604.24820/) (2026)
- [The Evolution of Attention in Large Language Models: Mechanisms, Trade-offs, and Emerging Trends](https://mlsanity.com/paper/2609.39661/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have driven significant advancements across diverse NLP tasks, with long-context models gaining prominence for handling extended inputs. However, the expanding key-value (KV) cache size required by Transformer architectures intensifies the memory constraints, particularly during the decoding phase, creating a significant bottleneck. Existing sparse attention mechanisms designed to address this bottleneck have two limitations: (1) they often fail to reliably identify the most relevant tokens for attention, and (2) they overlook the spatial coherence of token selection across consecutive Transformer layers, which can lead to performance degradation and substantial overhead in token selection. This paper introduces TidalDecode, a simple yet effective algorithm and system for fast and accurate LLM decoding through position persistent sparse attention. TidalDecode leverages the spatial coherence of tokens selected by existing sparse attention methods and introduces a few token selection layers that perform full attention to identify the tokens with the highest attention scores, while all other layers perform sparse attention with the pre-selected tokens. This design enables TidalDecode to substantially reduce the overhead of token selection for sparse attention without sacrificing the quality of the generated results. Evaluation on a diverse set of LLMs and tasks shows that TidalDecode closely matches the generative performance of full attention methods while reducing the LLM decoding latency by up to 2.1x.