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When we started Cerebras, we knew we would have to solve difficult problems in yield, power, and cooling. What became clear was that making wafer-scale work would require rethinking almost the entire system. Today, we answered questions from the community about those engineering decisions, how we run larger models,... show more

Sean Lie

15,863 subscribers

32,611 просмотров • 1 месяц назад •via X (Twitter)

Наука и технологии Образование

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Комментарии: 12

Фото профиля Papa Johns

Papa Johns1 месяц назад

@cerebras @MilksandMatcha Thanks, Sean, for the insightful sharing. 👏

Фото профиля Abhishek Tiwari

Abhishek Tiwari1 месяц назад

@cerebras @MilksandMatcha I like how this is very accessible for non super technical folks.

Фото профиля Sean Lie

Sean Lie1 месяц назад

@cerebras @MilksandMatcha thank you

Фото профиля Sanjeev

Sanjeev1 месяц назад

@cerebras @MilksandMatcha Very informative!

Фото профиля Engin

Engin1 месяц назад

@cerebras @MilksandMatcha Because it is really cool!

Фото профиля Mustafa

Mustafa1 месяц назад

@cerebras @MilksandMatcha The telescope became powerful not merely because its glass improved, but because the entire observatory was designed around precision.

Фото профиля Zeek | Insights 📊

Zeek | Insights 📊1 месяц назад

@BitcoinPierre @cerebras @MilksandMatcha Let me dive in

Фото профиля Michael Farber

Michael Farber1 месяц назад

@cerebras @MilksandMatcha Super helpful.

Фото профиля Elias Mufarech

Elias Mufarech1 месяц назад

@cerebras @MilksandMatcha good example of how every company is now a media company... and how easy it is to do it!

Фото профиля am.will

am.will1 месяц назад

@cerebras @MilksandMatcha great video!

Фото профиля Priyank Mishra

Priyank Mishra1 месяц назад

@cerebras @MilksandMatcha the SRAM over HBM choice is the interesting one. if wafer-scale actually scales, some of the HBM demand story has a substitute nobody's pricing in. tiny share today, but it's a real one.

Фото профиля Anton Trunov

Anton Trunov1 месяц назад

@cerebras @MilksandMatcha Amazing overview! One of my friends got really excited when I joined Cerebras and has been nudging me since then to ask a cooling-related question: have you considered using 3M Novec or something better in the future?

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I joined AMD right after graduating from MIT. Two stints there and a decade building Cerebras later, I still lose sleep over how much memory and compute to put on a chip. I’ve spent my entire career in computer architecture, and I’ve never seen the problems change this quickly. Models keep getting larger. The demands on the hardware keep growing. Decisions about where to put memory and how to connect it have become central to what people can build with AI. We chose SRAM for its bandwidth. Now we’re working on wafer-scale stacked DRAM to give larger models more capacity while preserving fast inference. That brings us back to some very physical problems: how to power it, cool it, and manufacture it reliably. This is the work I love. In part two of our AMA, I answered your questions about those decisions and where we’re going next. 0:30 What changes between CS4, CS5, and CS6? 2:19 HBM already stacks memory. What’s different here? 3:34 We chose SRAM for speed. Why add DRAM? 4:52 Drawing the stack is easy. Making it work isn’t. 7:23 The memory-versus-compute decision that keeps me up at night

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