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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 görüntüleme • 1 ay önce •via X (Twitter)

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Papa Johns profil fotoğrafı

Papa Johns1 ay önce

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

Abhishek Tiwari profil fotoğrafı

Abhishek Tiwari1 ay önce

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

Sean Lie profil fotoğrafı

Sean Lie1 ay önce

@cerebras @MilksandMatcha thank you

Sanjeev profil fotoğrafı

Sanjeev1 ay önce

@cerebras @MilksandMatcha Very informative!

Engin profil fotoğrafı

Engin1 ay önce

@cerebras @MilksandMatcha Because it is really cool!

Mustafa profil fotoğrafı

Mustafa1 ay önce

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

Zeek | Insights 📊 profil fotoğrafı

Zeek | Insights 📊1 ay önce

@BitcoinPierre @cerebras @MilksandMatcha Let me dive in

Michael Farber profil fotoğrafı

Michael Farber1 ay önce

@cerebras @MilksandMatcha Super helpful.

Elias Mufarech profil fotoğrafı

Elias Mufarech1 ay önce

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

am.will profil fotoğrafı

am.will1 ay önce

@cerebras @MilksandMatcha great video!

Priyank Mishra profil fotoğrafı

Priyank Mishra1 ay önce

@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.

Anton Trunov profil fotoğrafı

Anton Trunov1 ay önce

@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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We asked James Wang why Cerebras can run large models so quickly. His answer: Inference speed is mostly a memory problem. Instead of constantly pulling weights from external memory, Cerebras splits the model across multiple wafers and pipelines the layers together. “Inference is all about memory bandwidth.” “You don’t want to store weights external to the wafer, because the second it’s outside, it becomes much slower.” “We put the weights on the inside.” “The team built a new software stack that basically lets us split the models by layer and store them layer by layer across multiple wafers.” “That allows us to never have to read from external memory, and that’s what makes it so fast.” “For context, Claude is about 100 tokens per second.”

We asked James Wang why Cerebras can run large models so quickly. His answer: Inference speed is mostly a memory problem. Instead of constantly pulling weights from external memory, Cerebras splits the model across multiple wafers and pipelines the layers together. “Inference is all about memory bandwidth.” “You don’t want to store weights external to the wafer, because the second it’s outside, it becomes much slower.” “We put the weights on the inside.” “The team built a new software stack that basically lets us split the models by layer and store them layer by layer across multiple wafers.” “That allows us to never have to read from external memory, and that’s what makes it so fast.” “For context, Claude is about 100 tokens per second.”

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