do i know how it works? no. do i have any interest in how it works? no. does it actually do what FlashAttention does? no idea. do i have tests at the boundaries which tell me that it works? yes. would i know how FlashAttention worked if i just ran FlashAttention? also no.
November 1, 2026 at 10:03 PM
do i know how it works? no. do i have any interest in how it works? no. does it actually do what FlashAttention does? no idea. do i have tests at the boundaries which tell me that it works? yes. would i know how FlashAttention worked if i just ran FlashAttention? also no.
if i were using FlashAttention, though, i would just import it and trust that someone involved in the process understood how it worked. but now there is no one to certify that i am doing something sensible!
November 2, 2026 at 5:00 PM
if i were using FlashAttention, though, i would just import it and trust that someone involved in the process understood how it worked. but now there is no one to certify that i am doing something sensible!
New result: Automatic derivation of FlashAttention from vanilla attention in ~25 lines of SQL via three portable extensions.
github.com/xqlsystems/x...
github.com/xqlsystems/x...
October 30, 2026 at 3:17 PM
New result: Automatic derivation of FlashAttention from vanilla attention in ~25 lines of SQL via three portable extensions.
github.com/xqlsystems/x...
github.com/xqlsystems/x...
i really appreciate that i can take some fucked up architecture, have no idea how FlashAttention's cache streaming really works, and say "uh........ make FlashAttention but for this thing, plzthx," and have something which appears to let me use much larger sequence lengths when i get back from lunch
November 1, 2026 at 10:02 PM
i really appreciate that i can take some fucked up architecture, have no idea how FlashAttention's cache streaming really works, and say "uh........ make FlashAttention but for this thing, plzthx," and have something which appears to let me use much larger sequence lengths when i get back from lunch
like: FlashAttention doesn't work for Tan, et al.'s (2025) stick-breaking attention. i need streaming cache in order to test at higher context lengths. so i am relying on pure slopcode which not only _did_ i not read, i _cannot_ read.
bsky.app/profile/theo...
bsky.app/profile/theo...
i really appreciate that i can take some fucked up architecture, have no idea how FlashAttention's cache streaming really works, and say "uh........ make FlashAttention but for this thing, plzthx," and have something which appears to let me use much larger sequence lengths when i get back from lunch
November 2, 2026 at 4:59 PM
like: FlashAttention doesn't work for Tan, et al.'s (2025) stick-breaking attention. i need streaming cache in order to test at higher context lengths. so i am relying on pure slopcode which not only _did_ i not read, i _cannot_ read.
bsky.app/profile/theo...
bsky.app/profile/theo...
GPU MODE — 100+ Hands-On Lectures on CUDA, Triton, FlashAttention, vLLM, NCCL, SGLang, CuTeDSL + GPU Kernel!
November 5, 2026 at 4:00 PM
GPU MODE — 100+ Hands-On Lectures on CUDA, Triton, FlashAttention, vLLM, NCCL, SGLang, CuTeDSL + GPU Kernel!
Quentin Anthony, Ganesh Bikshandi, Praneeth Medepalli, Stephen Ebert: ZYFA: Speed and Accuracy of FP8 FlashAttention on AMD CDNA4 https://arxiv.org/abs/2611.07496 https://arxiv.org/pdf/2611.07496 https://arxiv.org/html/2611.07496
November 6, 2026 at 6:40 AM
Quentin Anthony, Ganesh Bikshandi, Praneeth Medepalli, Stephen Ebert: ZYFA: Speed and Accuracy of FP8 FlashAttention on AMD CDNA4 https://arxiv.org/abs/2611.07496 https://arxiv.org/pdf/2611.07496 https://arxiv.org/html/2611.07496
BlueSkyでの投稿用に、専門的かつ分析的な視点を維持しつつ、技術コミュニティの関心を引くキャッチーな要約を作成しました。スレッド形式（全4〜5投稿）を想定しています。
---
### BlueSky 投稿案：FlashAttention v1〜v4の変遷と分析
**【Post 1: イントロダクション】**
LLMの長文コンテキスト対応を支える核心技術「FlashAttention」。v1から最新のv4まで、この技術がいかにしてGPUの物理的限界を突破してきたのか、その進化の軌跡を分析しました。単なる「高速化」ではなく、ハードウェア階層をハックし続けた歴史が...
---
### BlueSky 投稿案：FlashAttention v1〜v4の変遷と分析
**【Post 1: イントロダクション】**
LLMの長文コンテキスト対応を支える核心技術「FlashAttention」。v1から最新のv4まで、この技術がいかにしてGPUの物理的限界を突破してきたのか、その進化の軌跡を分析しました。単なる「高速化」ではなく、ハードウェア階層をハックし続けた歴史が...
November 1, 2026 at 8:17 AM
BlueSkyでの投稿用に、専門的かつ分析的な視点を維持しつつ、技術コミュニティの関心を引くキャッチーな要約を作成しました。スレッド形式（全4〜5投稿）を想定しています。
---
### BlueSky 投稿案：FlashAttention v1〜v4の変遷と分析
**【Post 1: イントロダクション】**
LLMの長文コンテキスト対応を支える核心技術「FlashAttention」。v1から最新のv4まで、この技術がいかにしてGPUの物理的限界を突破してきたのか、その進化の軌跡を分析しました。単なる「高速化」ではなく、ハードウェア階層をハックし続けた歴史が...
---
### BlueSky 投稿案：FlashAttention v1〜v4の変遷と分析
**【Post 1: イントロダクション】**
LLMの長文コンテキスト対応を支える核心技術「FlashAttention」。v1から最新のv4まで、この技術がいかにしてGPUの物理的限界を突破してきたのか、その進化の軌跡を分析しました。単なる「高速化」ではなく、ハードウェア階層をハックし続けた歴史が...
Une petite démonstration publiée sur Hacker News attire l’attention des chercheurs en IA et des ingénieurs système : à partir d’une requête SQL de seulement vingt‑cinq lignes, le projet génère automatiquement un noyau FlashAttention.
👉 Lire l'article complet 👇
#IA #AI
👉 Lire l'article complet 👇
#IA #AI
Quand le SQL façonne le prochain noyau FlashAttention
Un projet open‑source montre comment 25 lignes de SQL peuvent générer un noyau FlashAttention, grâce à trois extensions SQL dédiées aux tenseurs.
aihealth.fr
October 30, 2026 at 10:07 PM
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL | Discussion
xarray-sql/benchmarks/ml/01_attention.py at 6debe2495dcb4e8fac20ab97df5b89baae2a31a6 · xqlsystems/xarray-sql
An experiment to query Xarray datasets with SQL. Contribute to xqlsystems/xarray-sql development by creating an account on GitHub.
github.com
October 30, 2026 at 9:40 PM
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL | Discussion
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL
Discussion | hackernews | Author: alxmrs
#Databases
Discussion | hackernews | Author: alxmrs
#Databases
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL
An experiment to query Xarray datasets with SQL. Contribute to xqlsystems/xarray-sql development by creating an account on GitHub.
github.com
October 30, 2026 at 8:41 PM
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL
Discussion | hackernews | Author: alxmrs
#Databases
Discussion | hackernews | Author: alxmrs
#Databases
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL
https://github.com/xqlsystems/xarray-sql/blob/6debe2495dcb4e8fac20ab97df5b89baae2a31a6/benchmarks/ml/01_attention.py
https://github.com/xqlsystems/xarray-sql/blob/6debe2495dcb4e8fac20ab97df5b89baae2a31a6/benchmarks/ml/01_attention.py
October 30, 2026 at 8:40 PM
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL
https://github.com/xqlsystems/xarray-sql/blob/6debe2495dcb4e8fac20ab97df5b89baae2a31a6/benchmarks/ml/01_attention.py
https://github.com/xqlsystems/xarray-sql/blob/6debe2495dcb4e8fac20ab97df5b89baae2a31a6/benchmarks/ml/01_attention.py
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL
comments · posted on 2026.10.30 at 16:09:47 (c=0, p=4)
comments · posted on 2026.10.30 at 16:09:47 (c=0, p=4)
October 30, 2026 at 8:29 PM
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL
comments · posted on 2026.10.30 at 16:09:47 (c=0, p=4)
comments · posted on 2026.10.30 at 16:09:47 (c=0, p=4)
No BF16, no FlashAttention-2 — the fast paths skip Volta, so the maths has to carry it. Chapter one, though: the throughput numbers are still pending. Hard to see this beating a rented 4090 on tokens per dollar once the power bill lands.
Running a 27B Large Model on Three V100s: Inference Optimisation from First Principles and in Practice [Chapter One]
A hands-on technical explainer on deploying Qwen3.8-27B across three 2017-era V100 GPUs, covering prefill and decode bottlenecks, KV cache, parallelism, quantisation and the practical limits of old hardware.
aichina.news
November 5, 2026 at 9:50 AM
No BF16, no FlashAttention-2 — the fast paths skip Volta, so the maths has to carry it. Chapter one, though: the throughput numbers are still pending. Hard to see this beating a rented 4090 on tokens per dollar once the power bill lands.
Deriving a FlashAttention kernel from a short SQL spec is a fun angle. Curious how you check that the generated kernel matches a hand-written one numerically, and whether the tiling falls out of the SQL or needs hints.
October 31, 2026 at 6:44 AM
Deriving a FlashAttention kernel from a short SQL spec is a fun angle. Curious how you check that the generated kernel matches a hand-written one numerically, and whether the tiling falls out of the SQL or needs hints.
@davidcrespo.bsky.social People treated FP16 naive serving on A100s as a hard floor. FlashAttention-2 plus vLLM PagedAttention alone collapsed memory-bandwidth stalls by 4.2x, turning a $0.02/1k token loss into positive unit margin overnight.
kinda forgot about this because I haven't heard it in a while, but do you remember how many people insisted no one would be paying for inference if they had to pay what it cost to provide
oh this is a great one. more than 80% of their revenue in H1 2026 is "consumption-based", i.e., enterprises paying per token at API rates
October 30, 2026 at 6:50 AM
@davidcrespo.bsky.social People treated FP16 naive serving on A100s as a hard floor. FlashAttention-2 plus vLLM PagedAttention alone collapsed memory-bandwidth stalls by 4.2x, turning a $0.02/1k token loss into positive unit margin overnight.
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL https://github.com/xqlsystems/xarray-sql/blob/6debe2495dcb4e8fac20ab97df5b89baae2a31a6/benchmarks/ml/01_attention.py (https://news.ycombinator.com/item?id=50309479)
xarray-sql/benchmarks/ml/01_attention.py at 6debe2495dcb4e8fac20ab97df5b89baae2a31a6 · xqlsystems/xarray-sql
An experiment to query Xarray datasets with SQL. Contribute to xqlsystems/xarray-sql development by creating an account on GitHub.
github.com
October 30, 2026 at 10:53 PM
Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL https://github.com/xqlsystems/xarray-sql/blob/6debe2495dcb4e8fac20ab97df5b89baae2a31a6/benchmarks/ml/01_attention.py (https://news.ycombinator.com/item?id=50309479)
Found this project for NVIDIA RTX 30-series GPU users running local AI: github.com/Zagitta/gpu-...
gpu-unlock targets matrix-heavy workloads like LLM prefill, diffusion, and FlashAttention, and promises interesting speed gains.
Curious to test it on a 3090.
gpu-unlock targets matrix-heavy workloads like LLM prefill, diffusion, and FlashAttention, and promises interesting speed gains.
Curious to test it on a 3090.
GitHub - Zagitta/gpu-unlock: Unlock FP/BF16 instruction issue rate on Nvidia 3000's cards to their professional equivalents
Unlock FP/BF16 instruction issue rate on Nvidia 3000's cards to their professional equivalents - Zagitta/gpu-unlock
github.com
October 26, 2026 at 10:46 AM
Found this project for NVIDIA RTX 30-series GPU users running local AI: github.com/Zagitta/gpu-...
gpu-unlock targets matrix-heavy workloads like LLM prefill, diffusion, and FlashAttention, and promises interesting speed gains.
Curious to test it on a 3090.
gpu-unlock targets matrix-heavy workloads like LLM prefill, diffusion, and FlashAttention, and promises interesting speed gains.
Curious to test it on a 3090.
#PaperOfTheDay: FlashAttention (Dao et al., 2022)
Attention was slow mostly from memory traffic, not math. FlashAttention computes it in tiles inside fast on-chip SRAM and never writes the full N×N matrix to GPU memory. Same exact result, far fewer reads/writes.
knowyourml.com/report.php
Attention was slow mostly from memory traffic, not math. FlashAttention computes it in tiles inside fast on-chip SRAM and never writes the full N×N matrix to GPU memory. Same exact result, far fewer reads/writes.
knowyourml.com/report.php
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — Know Your ML
FlashAttention tiles exact attention in fast on-chip memory, cutting memory from quadratic to linear and making long-context transformers practical.
knowyourml.com
October 22, 2026 at 2:34 AM
#PaperOfTheDay: FlashAttention (Dao et al., 2022)
Attention was slow mostly from memory traffic, not math. FlashAttention computes it in tiles inside fast on-chip SRAM and never writes the full N×N matrix to GPU memory. Same exact result, far fewer reads/writes.
knowyourml.com/report.php
Attention was slow mostly from memory traffic, not math. FlashAttention computes it in tiles inside fast on-chip SRAM and never writes the full N×N matrix to GPU memory. Same exact result, far fewer reads/writes.
knowyourml.com/report.php
Emmanuel Irabor, Marc Casas: Reorienting the Vector Axis: Restructuring FlashAttention for Long-Vector Processors https://arxiv.org/abs/2610.29489 https://arxiv.org/pdf/2610.29489 https://arxiv.org/html/2610.29489
October 22, 2026 at 6:38 AM
Emmanuel Irabor, Marc Casas: Reorienting the Vector Axis: Restructuring FlashAttention for Long-Vector Processors https://arxiv.org/abs/2610.29489 https://arxiv.org/pdf/2610.29489 https://arxiv.org/html/2610.29489
📰 Reddit r/MachineLearning
Open GPU kernel engineering series: naive GEMM to FlashAttention, H100 and Blackwell (44 sessions, code public) [P]
https://www.reddit.com/r/MachineLearning/comments/1xa4h2g/open_gpu_kernel_engineering_series_naive_gemm_to/
#IA #AI #ML
Open GPU kernel engineering series: naive GEMM to FlashAttention, H100 and Blackwell (44 sessions, code public) [P]
https://www.reddit.com/r/MachineLearning/comments/1xa4h2g/open_gpu_kernel_engineering_series_naive_gemm_to/
#IA #AI #ML
October 21, 2026 at 3:01 PM
📰 Reddit r/MachineLearning
Open GPU kernel engineering series: naive GEMM to FlashAttention, H100 and Blackwell (44 sessions, code public) [P]
https://www.reddit.com/r/MachineLearning/comments/1xa4h2g/open_gpu_kernel_engineering_series_naive_gemm_to/
#IA #AI #ML
Open GPU kernel engineering series: naive GEMM to FlashAttention, H100 and Blackwell (44 sessions, code public) [P]
https://www.reddit.com/r/MachineLearning/comments/1xa4h2g/open_gpu_kernel_engineering_series_naive_gemm_to/
#IA #AI #ML
Das Paper erklärt, warum LLMs auf Basis der Transformer-Architektur so rechen- und energieintensiv sind. Ein Flaschenhals ist nicht nur die Anzahl der Berechnungen, sondern auch der Aufwand für das Laden, Speichern und Verschieben von Daten in der GPU.
arxiv.org/abs/2205.14135
arxiv.org/abs/2205.14135
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to addr...
arxiv.org
October 20, 2026 at 9:13 AM
Das Paper erklärt, warum LLMs auf Basis der Transformer-Architektur so rechen- und energieintensiv sind. Ein Flaschenhals ist nicht nur die Anzahl der Berechnungen, sondern auch der Aufwand für das Laden, Speichern und Verschieben von Daten in der GPU.
arxiv.org/abs/2205.14135
arxiv.org/abs/2205.14135