The Biggest Chip Ever Built — Why OpenAI Runs On It | Cerebras CEO Andrew Feldman
Episode publishedPodcast: The MAD Podcast with Matt Turc...The MAD Podcast with Matt Turck
Transcript preview
Preview only
This is the largest chip built in the history of the computer industry. It's 58 times larger than a GPU. And for AI, bigger chips process information more quickly, and therefore, you get answers in less time. For AI world, big chips are undoubtedly the best way to go. There's no moat in inference. It takes you eight keystrokes to move from a GPU to us in the cloud. We solved a problem that nobody in the history of computing solved, and we delivered it in 2020, and nobody cared. Nobody cared. Nobody bought it in. Nobody cared. Everybody said we were crazy. It would never work. So then we built
Hi. I'm Matt Turk. Welcome to the MAD podcast. My guest today is Andrew Feldman, cofounder and CEO of Cerubrus, the company that built the largest chip in the history of computing and just pulled off the biggest semiconductor IPO of all time. Andrew has been everywhere talking about the headlines, the $20,000,000,000 plus OpenAI deal, the IPO, but this conversation is a bit different. We started from what is a wafer and built up step by step, Why GPUs struggle with fast inference, the three shortages nobody talks about, the decade in the desert when nobody wanted this chip, and why Andrew beli
I thought a a fun place to start would be to talk about speed. So has speed become the dominant conversation for AI today?
Yeah. What happened, I think, was for a long time, AI was sort of a novelty. It was like a parlor trick. It was cool, but not useful. And what happened somewhere around the middle of twenty twenty five was the AI got smart enough such that people began to use it. And we remember, we we we make AI with training, but we use it with inference. And suddenly people wanted to use it. And the minute you wanna use it, the minute it's productive, right, speed matters. Right? Fast tokens are more productive. And so the conversation moved from everything else to how do we make our inference faster? How d
Great. And what what does speed mean? Is that is that a a question of token speed? Is that completion of the task? What what's the right metric? Sure.
The right metric is tokens per second per user. That that's how fast you get the first token all the way through the last token in in your in your response. And it's true for queries from from chat, but it's also true for for agentic floats. Right? If they're sort of multicycle turns, waiting is amplified. And so what you want is blisteringly fast responses so that the the AI feels like it's in real time. You can engage with this. K? So
it's the Brombeil moment for Post for AI. I think
that's I think that's right. And I I think that's a very good analogy. I I think if you think of something like like Netflix, right, when the Internet was slow, right, Netflix delivered DVDs and envelopes. You would get a a DVD at an envelope. And when the Internet became fast, they didn't get more efficient at delivering DMUDs and envelopes. They became a movie studio. Mhmm. Right? The speed enabled them to become something completely different. And that's what speed does in general and in particular for for AI. It it opens up a whole new domain. It allows you to use the AI differently. You w
Yep. So it's literally a question of of the UX. Right? That's just nobody wants to wait a few seconds.
Keep exploring with a free account.
A free account preserves the short preview. Upgrade to Pro for the full transcript and episode chat.