---
title: 'FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2307-08691-flashattention-2.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Tri Dao, FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: efficiency'
tags: arxiv, attention, efficiency, kernels
arxiv_id: 2307.08691
alias: 2307.08691-flashattention-2
type: pointer
format: pointer
sources:
  - id: S1
    title: 'FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning'
    publisher: arXiv
    authors: 'Tri Dao'
    url: https://arxiv.org/abs/2307.08691
    version_read: 'v1 (17 Jul 2023)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

## Summary
[FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://arxiv.org/abs/2307.08691) (2023) improved the original FlashAttention kernel with better parallelism and work partitioning. It became the default attention kernel in many modern training stacks.

What it introduced: a second-generation exact-attention kernel that reduces unused compute and improves scaling across thread blocks, with measured speedups over the first FlashAttention.

What it led to: widespread adoption in training frameworks and in libraries that expose attention backends. Live notes: [hf-transformers-attention-backends](https://iinventedlight.com/library/notes/hf-transformers-attention-backends/). The original FlashAttention paper is a pointer in batch B.

If you are choosing an attention kernel in a current training stack, this is the usual successor to the first FlashAttention paper, and the one most current libraries default to. Where to read it: https://arxiv.org/abs/2307.08691 (v1, 17 Jul 2023).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).