The document explains how FlashAttention computes attention in pieces, preserving the model’s allowed connections without storing the full score matrix. It previews the running calculation, GPU execution, training, token generation, and factors that limit speed gains.
The author describes a shift in LLMs from full attention toward hybrid and non-full-attention designs, citing an even split between those approaches in the second half of 2026. These methods can lower computation or memory costs for long contexts, though compressed history can make exact retrieval harder.
The piece analyzes the paper 'Attention Is Not What You Need,' using 30-day Hugging Face download data to compare attention-based, attention-free, and hybrid models, and concludes hybrids dominate the market while attention-free models struggle with tooling and ecosystem support.
The article uses roofline calculations to compare attention’s KV-cache traffic with GPU compute and memory limits. It argues that attention design affects whether decode benefits from GPUs or specialized hardware, while noting disagreements between its estimates and published work.
The paper presents HASA, a training-free sparse-attention method that adapts feature and token-region selection to each pretrained attention head. It reports 88.1% of dense-attention mass, higher benchmark accuracy than UNIQUE, and up to 14.07× speedup over dense attention.
AB-Attention provides a training-free method to approximate dense attention at high sparsity for video diffusion transformers, using intra-block clustering to assess block importance, probabilistic block sampling with inverse probability reweighting, and tensor-core friendly kernels to refine selections; it significantly reduces computation and improves video quality and speed compared to baselines, with code available online.
Latent Sparse Attention (LSA) is an open research release aimed at achieving subquadratic attention with accuracy comparable to full attention for ultra-long contexts. It organizes keys by meaning in a hierarchical manner, first checking high-level groups and then refining within the most relevant ones to find the needed keys.
The document presents a method to detect unsupported LLM responses using attention-based statistics computed without full attention matrices, achieving low overhead with FlashAttention-driven approaches and showing strong detector performance across benchmarks.
FlashAttention speeds up transformer attention by tiling computations to fit on-chip SRAM and using online softmax, reducing memory bandwidth bottlenecks without sacrificing precision. It recomputes gradients during the backward pass to avoid storing large intermediate matrices, yielding 2x–4x wall-clock time improvements over standard attention on GPUs.
Latent Sparse Attention (LSA) provides a subquadratic attention approach by clustering keys and scoring clusters, enabling faster long-context processing. Distilled (D-LSA) and Native (N-LSA) variants offer speedups and accuracy parity, with detailed installation and deployment steps for integrating LSA into serving pipelines.