Anoop Saha @asyncanoop
I correlate; therefore, I cause! 100k GPU cluster is all you need ÜT: 28.578672,77.23953 Joined April 2010-
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My Google colleagues Norm Jouppi & Sridhar Lakshmanamurthy gave a talk today at Hot Chips on TPUv7 ("Ironwood"). The TPUv7 system offers 9216 chips / pod (42.5 exaflops of fp8), but we can scale across many of these pods to provide multiple zettaflops.
My Google colleagues Norm Jouppi & Sridhar Lakshmanamurthy gave a talk today at Hot Chips on TPUv7 ("Ironwood"). The TPUv7 system offers 9216 chips / pod (42.5 exaflops of fp8), but we can scale across many of these pods to provide multiple zettaflops.
It’s Hot Chips day!! Looking forward to the talks this afternoon…
This is an excellent overview. I am surprised despite all the growth in AI, how little material there is in core GPU architecture. The JAX team changes it for good.
This is an excellent overview. I am surprised despite all the growth in AI, how little material there is in core GPU architecture. The JAX team changes it for good.
Amazon’s chip division is working with Taiwan’s Alchip on Trainium3 and Trainium4 to bring the chips to mass production on TSMC 3nm and 2nm processes, respectively, media report, citing unnamed industry sources. Trainium3 will be in mass production in the 1st quarter of 2026.…
We are not done with scaling laws yet. While test time scaling will be heterogenous and most likely result in deployment of new architectures and new systems, the next unlock in LLMs is by scaling up post training, particularly RLVR. arxiv.org/pdf/2505.24864
Millions of Americans, red and blue, young and old, men and women, will die because some asshats are in charge of science funding.
Millions of Americans, red and blue, young and old, men and women, will die because some asshats are in charge of science funding.
The biggest bottleneck in scaling laws is RL compute efficiency (or lack of it). Whoever solves that would bring us closer to AGI.
The biggest bottleneck in scaling laws is RL compute efficiency (or lack of it). Whoever solves that would bring us closer to AGI.
In other words, discrete accelerators will always be better than integrated ones. Sharing bus bandwidth with CPU and limited dram bandwidth will be a drag.
In other words, discrete accelerators will always be better than integrated ones. Sharing bus bandwidth with CPU and limited dram bandwidth will be a drag.
"Runs on a high end laptop" - Excellent stuff.
We need a larger on-device AI community. That will be the future.
Test cricket… absolute goosebumps. Series 2–2, Performance 10/10! SUPERMEN from INDIA! What a Win. 💙🇮🇳🏏
Watch out for the compute in memory architectures. It’s closer than we think it is.
Watch out for the compute in memory architectures. It’s closer than we think it is.
Interesting bit from @Kimi_Moonshot technical report for optimizing start times for both training and inference nodes. I would love to see the numbers from these optimizations. github.com/MoonshotAI/Kim…
I have said it before and I will say it again. Going to office and meeting colleagues and customers in person has a disproportionately high RoI!! For the individual..
This is easily the best set of lectures in AI. It goes deep into GPU architecture to model design to distributed computing, combining multiple fields into one.
This is easily the best set of lectures in AI. It goes deep into GPU architecture to model design to distributed computing, combining multiple fields into one.
What you need to read, if you want to work with LLMs on scale.
“San Francisco is a mindset. The home of AI” - Mayor @DanielLurie at the #whartonforum. And as the capital of AI, the only mindset you need is that you will use your GPUs efficiently.
Leadership needs courage. Indomitable @vkhosla at the #whartonforum
torchft + TorchTitan: 1200+ failures, no checkpoints, model convergence. A Llama 3 model was trained across 300 L40S GPUs with synthetic failures every 15s. No restarts. No rollbacks. Just asynchronous recovery and continued progress. 📘 hubs.la/Q03t1Z0b0 #PyTorch…
Never will be.
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