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Flash Attention Explained!

Audience:

Transformers are now everywhere—from generating game frames and AI avatars to powering YouTube's new LLM-based recommendation algorithm—and at the heart of it all is the attention mechanism. But as models scale, the original attention operation becomes a costly bottleneck. Enter Flash Attention, a breakthrough algorithm from Tri Dao that drastically speeds up attention by optimizing how data flows through GPU memory. In this video, we explore the core ideas behind scaled dot-product attention, the limitations imposed by memory bandwidth, and how Flash Attention uses online softmax and clever memory-aware buffering to reduce quadratic memory complexity to linear. You'll learn how the GPU memory hierarchy (L1, L2, HBM) affects performance, how Flash Attention keeps everything in cache-friendly form, and why even consumer GPUs like the RTX 3090 see over 4x speed-ups with no compromises in accuracy. We also break down how this works mathematically, what makes it so elegant, and how to enable it in PyTorch with just one line of code. If you care about scaling ML, maximizing your hardware, or just want a deeper appreciation of the fusion between algorithm design and GPU architecture, this one’s for you.

Analytics

6.34 Overall score*

63 Rank

16 Votes

11 Comments

Comments

3.5

Too much time spent on explaining a fairly straightforward idea…

5.7

Computer architecture knowledge was expected from the viewer which is not unjustified because this is a video regarding AI. However, this does focus on the math on how memory is being saved using an algorithm to calculate the final matrix without the weights matrix. Some form of simplification would have been nice. The visual at 17:00 would have been nice to see at the start because it helps provide a visual of what B S D mean. In contrast, the math was made to be relatively easy to understand and the explanations were very clear. Perhaps I was not the true target audience for this video because a CS or EE student may have found this topic more accessible. Good video nonetheless.

8.5

Awesome! I love the balance between good visuals, clear explainations with gradula complexity and a good opener that shows there is way more to uncover.

Great video overall on a difficult topic to explain cleanly.

I’d say that it was hard comprehending the narration while watching the visuals, especially in scenes depicting the computational workflow where words sometimes didn’t align with the animation. Many acronyms or shorthands like “mat mul” make it not as “empathetic” to the uninitiated audience. Names for values like “S” that don’t have a corresponding symbol on screen, or certain workflow steps being done without much prior context. So, I’d say watching this video required a good amount of pre-knowledge, especially for undergraduates.

I definitely think you achieved explaining the novel mathematical tricks lending to online softmax. And you were clear about what else there would be to cover in order to fully understand the flash attention algorithm.

You have a calm, patient explanatory style, while also have great videography. I think I’ll remember from your video the importance of working with the properties of different GPU memories in mind. Great work!

6.5

I thought this was a nice understandable video about an interesting kind of optimization, with a friendly, inviting style. “Literally every corner of our lives” seems pretty hyperbolic in a celebratory way that I didn’t like.

5.3

Soooo much algebra… at the start of the video I was intrigued because machine learning is a trendy topic I woud like to learn about attention, flash attention, etc. But I felt there was a lot of machine learning jargon that I was assumed to know. It would’ve been nice to explain what are attention, softmax, etc. Somewhere in the middle in the video the algebra stopped sounding like real words so I just listened along. But I think at least the video was well edited, and I took away the high level message about optimizing ML computation for the caching structure of the GPU.

7.5

Great explanation of a numerical algorithm that trades computations for memory. The explanation and animations are clear.

6.2

Good demo of how softmax is done. Could be shorter.

6.9

I think this is an interesting video on the intersection between algorithms and how actual speed up occurs by finding a solution to different problem than in theory. Overall, could follow the concepts quite well and understand the basic idea of how it works and where the speed up is coming from. I think that there were some details that are slightly hazy and maybe a demonstration on how it was before and after and the additional steps taken to materialize the matrix visually might help to get other viewers to get the aha moment more clearly.

7.8

I’m a fan of work covering/explaining recent papers, so this was nice. I think you labeled your audience correctly as undergrad/grad. This gets into a lot of weeds of both computer science and (what I’d consider) advanced algebra. This is also topical, which is a nice plus. You make a good effort to consistently enforce the motivation, which is appreciated when there’s so much raw calculation going on. Maybe this is old hat, but I really like the fact that e^x allows for all of these interesting tricks. The consistent examples was also very helpful. The only reason I don’t rate higher is due to my own limitations to understanding the topic. As well, I might have added to the section starting at 17:00 (The Flash Attention Algorithm) a visual of how those numbers were moving through your 3-teir hierarchy to show how it only makes one loop. Really wonderful work.

This seems like it will be relevant to me, as somebody who is interested in both hardware and AI!