Hello, I'm John Furrier with theCUBE. We are here in San Francisco at Fort Mason. We are here for the Supernova event. Cerebras is having a big news launch. It's turning into almost like a conference. A lot of people coming, oversubscribed event, a lot of buzz. And of course Andrew Feldman is the star of the show. The founder and CEO of Cerebras. Andrew, great to see you again.
It's good to see you again. It's our pop-up cube. But we will do whatever it takes to get the stories that matter.
We appreciate that. We appreciate that. So obviously we've had a couple conversations before. You guys really on a tear recently. Meta announcement, I saw the IBM news, just continuing to thunder away at pushing the envelope on the speed. And this is not new for people who know what you've been doing and saying speed matters. The market wants more. They want more faster. Slow is bad, fast is good. Simple message, but hard to do. What's the core news that you guys are launching here today?
Well, we're putting out a new model, Qwen 32B, and as with all our models, it is the fastest in the industry, bar none. Somewhere between 20 and 30 times faster than the fastest GPU offering. Qwen's really interesting model. It's a reasoning model and it's one of the smartest models. It behaves in line with the leading closed source models, while it's an open source model. So fast model, a smart model. I think it'll give some OpenAI's models a real run for their money.
You've obviously been at this and seeing all the, I call it the AI Olympics. Say, 'I got the faster thing.' A lot has changed. Reasoning is putting a lot of pressure on the tokens and so there are some key requirements that people are paying attention to. Could you share in your mind the key requirement that people should pay attention to? We had Russ on, one of your early partners here, from LiveKit. He saw my first token out. How fast you get those tokens. Speed of course matters, but what are some of the things that people should pay attention to? Because it seems like the goal posts are shifting a lot. People are taking advantage of the stats, saying certain things. What do you advise people to look at as you guys push the envelope on being the fastest?
Reasoning models behave much more like humans, right? If you set out to write a paper, you don't just disgorge the paper. You don't just think for a second and then write the whole paper. Likely you'd write a draft, you'd edit it, you'd think about it again, you'd review it and you would iterate. Reasoning models perform that workflow. They develop an answer, they review it, they improve it. And this requires more work. And more tokens. That's right. And tokens are just a measure of computational work. And so nobody really wants to wait 30 minutes for their research report to come out, right? You'd like it out extremely quickly. And so for this type of work, for agentic workflows, for the reasoning for the smartest models, performance is particularly important because you have so many more tokens to work through. So if you are 20 or 30 times faster, you get something in a second or two instead of several minutes.
In your talk, you've got some stats and then always the bar charts. Break that down for me. Simplify what the message is. Is it first token out? What are some of the key bullet points and stats for the new model?
So you spoke to some of our customers and partners. I think it varies based on your workload. For somebody who does voice, time to first token is a crucial measure. How fast can you get the first part of the response? I think for most other applications, output speed is the best measure. How fast can you finish the work? And that's measured by output speed. I think the best place to look is you can go every day and you can look on Artificial Analysis. They're a thoughtful benchmarking firm. They go to everybody's API, they ping it without you knowing it, and they report the results every single day. That's a good way to measure us.
So I got to ask you about the application. I love talking to your partners because you had a new AI entrepreneurial class. I was just making a comment on a keynote I was moderating this morning across town in San Francisco and I said when I was breaking, my first startup was an internet startup, they called it. I was an internet entrepreneur and then web entrepreneur, whatever you want to call it. But it was categorical. Now AI entrepreneurs are coming to the scene. You're attracting a lot of them. You also got the big partners. So it's like this big old and new. Or big and small. So what is an AI entrepreneur and what are the use cases that you're seeing that you guys are enabling? Because now that the horsepower is coming out, speed... what use cases are coming out? What kinds of use cases and what kind of startups are emerging in your mind?
Let me share with you several different examples. One from the largest and one from the smallest. We'll take Kim Branson at GlaxoSmithKline. They're a 300-year-old company. They're a leader in the design and development of pharmaceuticals. They've built an agentic flow that can ask the entire corpus of literature a question. The hundreds of thousands of papers or millions of papers that have been written around the world in any language, they can distill that down. They can then develop a hypothesis. They can then have pros and cons of the hypothesis. It can then search data in support of the hypothesis and eventually it will be able to link to automated labs, which could then run experiments in support of the hypothesis. That's right. On robots. That's right. And it asked a question, it scoured the existing knowledge. It played the role of a professor, of a researcher. It scoured what was known, it thought about, it delivered some questions to the community. It then went out and it answered them. And it then criticized itself. It critiqued itself, whether it was a good answer, good hypothesis or not.
You bring up a good point that comes up a lot. The robotics is hot right now, but it's also phase one was get the machines right, get the computer vision. Now that's a lot of machine learning, a lot of tech. Now the open source software world is now plugging in to these robots as a software interface. So smaller, faster, cheaper hardware. That's like the Moore's Law world, you live and make it faster, make next version better. But the software has to get smarter, more intelligent and smaller code.
Sure. For those robots, understanding the world around them is the fundamental challenge. It is a big eater of inference compute for sure. For sure.
Okay, so what's the relationship with Meta? I just want to review a little bit of some past couple of weeks for Cerebras. You had some good run... You got a good run in. Not a bad week for you guys, or a couple of weeks. Take us through what's happened because a lot of breakthroughs happened. Just walk us through storyboard. You had the Meta LlamaCon, you had that event and then IBM, a bunch of other things. Take us through the news.
Sure. So Meta announced that they would be providing an API-based service. So you could go to their API and it would serve Llama 4. And that behind their API was Cerebras. So they would be our fast tokens that they were being served through their offering. So this was the first hyperscaler win for Cerebras. Immediately following that, IBM announced that they would be doing approximately the same thing. We would be providing fast inference to their clients. So if you think of Meta as at one end, developer friendly, mobile first, developer friendly and huge. Huge. Huge. And then IBM does business with the largest 100 customers in the world, largest accounts. We have expanded our reach from the developer all the way to the largest financial institutions in the world through IBM.
