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Two days ago, Deepseek surprised everyone with an "undefined-behavior" PTX optimization speeding up particular ML workloads on a Hopper NVIDIA GPU Kernel. Let's reverse engineer the hack, implement it ourselves, and benchmark the speedup on an H100.

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LaurieWired

155,104 subscribers

228,591 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 11

Фото профиля LaurieWired
LaurieWired1 год назад

Full Video:

Фото профиля LaurieWired
LaurieWired1 год назад

My test code:

Фото профиля NetMind.AI
NetMind.AI2 лет назад

Get access to a wide range of GPUs like H100, A100, 4090, 3090 and save over 90% at NetMind Power. Rent Now!

Фото профиля numanumabruh
numanumabruh1 год назад

You'd never know she's 6'5"

Фото профиля Jason Ho
Jason Ho1 год назад

laurie supremacy

Фото профиля Bob (Moderna #7) Kerns
Bob (Moderna #7) Kerns1 год назад

Until recently, I'd only seen your tweets; the first video I encountered was the 2025 prediction ones. Assumptions violated: higher voice, younger. Always good to have one's assumptions flagged, but especially the age. I was struck by the maturity of your analysis!

Фото профиля Dave 🚀
Dave 🚀1 год назад

LFG!

Фото профиля KnowledgeisMostValuable
KnowledgeisMostValuable1 год назад

I'd fight off a bear for you

Фото профиля nisten - e/acc
nisten - e/acc1 год назад

lfg

Фото профиля 🥀shiVam🥀
🥀shiVam🥀1 год назад

wait, did you film this at Google HQ? (must appreciate the audio recording and editing)

Фото профиля Calcs
Calcs1 год назад

Fantastic video, more please, lol 😂

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Just built an MCP for Ghidra. Now basically any LLM (Claude, Gemini, local...) can Reverse Engineer malware for you. With the right prompting, it automates a *ton* of tedious tasks. One-shot markups of entire binaries with just a click. Open source, on Github now.

LaurieWired

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a Tsinghua University lab just put a project on GitHub that replaces a $400,000 H100 rack with a single 24GB graphics card. it's called ktransformers, and the trick is almost stupidly simple: the experts you actually use stay on the gpu, the ones you don't sit on the cpu until they're called / deepseek-v3 and r1 with 139K context in 24gb of vram / up to 28x speedup over the standard setup / fine-tune deepseek-v3 across four rtx 4090s instead of a datacenter / built by tsinghua university's madsys lab, not a startup with a landing page apache 2.0, and already past 17,000 stars. -> bookmark it.
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a Tsinghua University lab just put a project on GitHub that replaces a $400,000 H100 rack with a single 24GB graphics card. it's called ktransformers, and the trick is almost stupidly simple: the experts you actually use stay on the gpu, the ones you don't sit on the cpu until they're called / deepseek-v3 and r1 with 139K context in 24gb of vram / up to 28x speedup over the standard setup / fine-tune deepseek-v3 across four rtx 4090s instead of a datacenter / built by tsinghua university's madsys lab, not a startup with a landing page apache 2.0, and already past 17,000 stars. -> bookmark it.

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Timelapse #177 (14 hrs) - Picked up an RTX Pro 6000 Blackwell and wired in another 1000W PSU - running two of them now - DeepSeek v4 Flash running locally on both GPUs - Distilling DeepSeek v4 Flash down into Nanbeige (still cooking) - Kernel bench runs for DeepSeek v4 Flash and Qwen 3.8 Max - Got it into chat but couldn't reliably get it into the OMP harness yet - Private contract work - days like this are stuff I actually want to build. and when it's the high-intensity grind, i still have to show up and get it done
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Timelapse #177 (14 hrs) - Picked up an RTX Pro 6000 Blackwell and wired in another 1000W PSU - running two of them now - DeepSeek v4 Flash running locally on both GPUs - Distilling DeepSeek v4 Flash down into Nanbeige (still cooking) - Kernel bench runs for DeepSeek v4 Flash and Qwen 3.8 Max - Got it into chat but couldn't reliably get it into the OMP harness yet - Private contract work - days like this are stuff I actually want to build. and when it's the high-intensity grind, i still have to show up and get it done

Elliot Arledge

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YOUR PARENTS PAID FOR THE CUDA MOAT! The #1 contributor to the CUDA MOAT isn't the the developers at NVIDIA, but it is the millions of developers outside of NVIDIA that invent new algorithms for CUDA like Flash Attention. For most of them, it started with an GeForce gaming GPU. NVIDIA is the only companies that has an reasonable good developer stack on consumer grade GPUs. As people grow up beyond playing CSGO & League of Legends & Minecraft, they either become anime weeaboos or they start programming on their existing computer with has an GeForce GPU
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YOUR PARENTS PAID FOR THE CUDA MOAT! The #1 contributor to the CUDA MOAT isn't the the developers at NVIDIA, but it is the millions of developers outside of NVIDIA that invent new algorithms for CUDA like Flash Attention. For most of them, it started with an GeForce gaming GPU. NVIDIA is the only companies that has an reasonable good developer stack on consumer grade GPUs. As people grow up beyond playing CSGO & League of Legends & Minecraft, they either become anime weeaboos or they start programming on their existing computer with has an GeForce GPU

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Quick video on the stages of reverse engineering🙂
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Quick video on the stages of reverse engineering🙂

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