Please welcome Cerebras CEO and co-founder Andrew Feldman.
Welcome everyone. Thank you so much for coming. It is with such pride and joy we see all of you here today. And to point out that while we have years of experience in building the fastest AI, managing queues out in front of our building is new to us. So forgive us for the delays there. This has been a truly extraordinary last three or four months for us, right? The penalty of going public. Let's all stare at that for a little while and we can understand the cost of going public, right? This is it. Um, yeah. Right. Right. Um, going public is a truly extraordinary event. It allowed me to see engineers I've been working with for 20 years. It allowed me to know they actually own a coat and tie. We didn't know this. Um, it was a time to share with our families after a decade of work. Um, and it was sort of humbling in the sense that we're now able to step forward in a different way and participate in the biggest of the big leagues and be part of the show.
Now, we were able to do this in part because we rode this enormous wave. All right, if you close your eyes and think back three years ago, right, AI was a novelty. All right. It was sort of a parlor trick. It was something you showed your friends and didn't use at work and then it became useful. All right? And now it's a necessity. And in that necessity, speed, latency shapes what we can build. And real time AI feels like an active collaborator when you're engaged with it. Right? In AI, speed is productivity. And as you know, what we do at Cerebras is deliver the fastest inference speed in the industry. Now, when you have that speed, AI responds in real time. Users do more with AI. They stay longer. They run more interesting workloads. They solve more interesting problems. Speed makes new markets. And it allows new ideas to flourish.
But until recently, there was a trade-off. There was a trade-off between speed and throughput. Everybody wants speed, right? Nobody says, 'Give me something slower.' Or said differently, if you really want to punish a naughty 12-year-old, don't take away their phone. Set it to dialup speed, right? For a week, right? This drives good behavior. All right? You had a tradeoff. You had a tradeoff between smart and fast. All right? If you went fast, you go to smaller models, more specialized models. All right? But when speed is no longer a challenge, you can have both speed and intelligence. And then you can move AI to time-sensitive work. Then you can move it to new parts of the business. You can all sorts of new things are possible.
The underpinning of our performance is our wafer scale engine. And I can't share with you how many people along this journey told us it would never work. And when it worked, they said you could never make it at volume. And when we made it at volume, they said you'd never package it. And then JP figured out how to package it. And then they said you only had one customer in the government. And then they said after we had a sovereign cloud, they say you don't have a hyperscaler. And we want a hyperscaler. Say, 'Oh, you don't have a frontier lab.' And now we have a frontier lab. All right. That's the journey not just of Cerebras but for everybody who does work that is not obvious that is hard that is challenging this is at the foundation of our work.
But the foundation is insufficient. All right, to deliver fast AI right now you need a chip, you need system and a rack, you need to build massive clusters, and you need software to tie it together seamlessly. And a lot of what we're going to be talking about in the next hour and a bit, all right, is how we build systems, how those systems tie together into clusters to deliver industry-leading performance and set the new bar.
Now speed is no longer sort of an infrastructure metric. I want you guys to really think about that. All right? For a long time it was a benchmark. Look how fast we are. But right now it shapes how products are built and it changes fundamentally the user experience. AI as it becomes mainstream, users expect it to feel like the best software they've ever worked with. They expect AI not to be different. It's going to be in the same category as their favorite software tools. They want it to be immediate, fluid, and responsive. And when the response arrives before your attention can move, you stay in the problem. All right? And you keep building. And as an infrastructure builder, when you allow someone to run a software that allows them to build interesting things, you're proud. That's why you build infrastructure. All right? And that's why we do this.
Okay. Last week, OpenAI announced a first look at GPT-5.6 Soul Ultrafast mode. Ultrafast is a new service tier that runs OpenAI's most intelligent models at up to 14 times faster than standard. We're testing this with select customers to start. What this brings the industry for the first time is frontier intelligence instantly. Right? 14x speed removes the speed intelligence trade-off. You can now have both. There is only one place you can get this frontier speed, frontier intelligence, and instantaneous speed. Customers now complete more useful work per second.
Now I'm going to show you a little demo because that's what I want to do. Oh, new slide. Um, keep the CEO on his toes. Reorder the slide deck. Um, this is a cool slide. On the x-axis you have speed and on the y-axis you have intelligence. All right. You want to be up and to the right on both. You want to be smarter and faster. And that puts you up here. And the only thing up here is GPT-5.6 Soul Ultrafast running on Cerebras in the entire industry. The only thing here.
Now I'm going to show you a demo. What you're about to see is a speedrun of a benchmark called Humanity's Last Exam. And for those of you who worked on a PhD, this will be depressing because it will be fast. This is a leading industry benchmark. Okay? And it's designed to show graduate level reasoning and we will be on the left. There are 2500 questions. We're going to show one to start and then we're going to go to the entire set. Let's go.
Okay, we're done. First question. This is the other guys. All right, now they're done. Now what we're going to do is take the entire exam. We're done. And it took 11 hours, 11 minutes, and 26 seconds. Less than half a day. The other guys took more than 3 days.
As my old soccer coach could tell you, this is the only thing I've ever been fast at. So, I really appreciate the applause. Um, okay. What I'd like to do right now is invite on stage one of the leaders in the leading Frontier Lab. Uh, I'd like to invite Tibo on stage. Tibo is head of core products and platforms at OpenAI. And we'll go through a few questions and hear a little bit about what OpenAI is thinking around AI and ultrafast AI. Tibo.
My water. It's good to see you. It's good to see you. So if you ever think you're having a good year or a good several years, OpenAI success can disabuse you of that fact when you're growing as fast as any company in the history of capitalism has grown. Right? You're having a pretty good run. And what I understand right now is that you have 15 million people a week running Codex and ChatGPT agents. Um, awesome numbers. And these products have sort of made an enormous impact on us. I mean, if you walk around our lab, and I think this is true in most companies now, everybody's got a GPT screen up. All right. Um, talk a little bit about your strategy. Tell us a little bit about how things have been evolving and how you think of the landscape.
Sure. First of all, great to be here. Um, it seems like we coordinated on the naming of our models in Supernova. Soul Supernova.
We are trying to follow in the celestial themes.
It seems more coordinated than it is.
We are not that coordinated. Have no fear.
Um, for me the strategy has been like very simple like build the best models and then serve them at scale. And then recently we also realized well it's all going to be about agents. So, you know, you've got to build like the infrastructure to have agents run at scale, run safely, have like very aligned models, and then build delightful interfaces to those agents so that they can do actual work for you. Um, and up until recently, we kind of had these trade-offs where, you know, if you wanted to get something done fast, you had to serve like a smaller model and you had to serve it at lower latency. And then you always had like this decision to make of like you know hey maybe um you know I want something done now so I'm going to use like a smaller size model or you know like okay you know we can think a little bit more so we're going to use Soul and what I love with Ultrafast is kind of don't have to make that decision anymore. It kind of gives a glimpse of what's to come. I think reducing the cognitive load and just building something that is delightful just works is fast when it needs to and then can also run slower in the background you know if you don't need at larger scale. Yeah, I think that's super exciting and we're really proud of our partnership. Tell us a little bit about how Ultrafast sort of fits in with your product vision and strategy.
Yeah, we would love to. Um, we're not there yet, but we would love for Ultrafast to just be the default. I think it is a glimpse of what's to come.
Write that down, investment community. Um, it's like as you saw in the videos, it's quite spectacular when you're in front of it. The first time I demoed it to someone, they were like, 'Oh, this must be fake. Uh you just planted some fake data and like, you know, show me the back end.' I was like, 'No, it's just running on this hardware over there. This little awesome company, Cerebras, to build great chips.' Um so we're pushing very hard on reliability. Uh obviously you know this infrastructure that powers a very large chunk of you know like in the future like a very large chunk of the GDP you know must be reliable um but then also we're pushing on the frontier of like how fast we can make it and this requires rethinking the entire approach. Uh it's not just you know from the inference uh like the inference matters but also like the models like how do we train them how do we think about tool calls where do we see like new bottlenecks sort of like emerging so we rewrote a large part of stack for Ultrafast. Um, and you know, it was a delightful partnership.
Yeah, that's really cool when sort of your compute infrastructure is so fast, it pushes the software guys up the stack and then that now there's headroom and now that the hardware can get faster, right? That interplay is really a pleasure. When it started to work, the first thing that we did is like we gave Ultrafast to the engineers working on making Ultrafast work and then just cranking that loop.
And you told me something back there that when your engineers have a really important problem, they'll ask for Ultrafast.
Yes. Yes. Um everyone wants access to Ultrafast. Uh we reserve it for incidents. Um you know, be it like outages or security incidents. Um you know we have those just as any other company and like Ultrafast comes in handy. Um top priority projects we always have Ultrafast like provisioned for those engineers and then like important research efforts as well like you know get Ultrafast. Uh we don't yet uh we reserve a lot of our capacity as well like you know for serving it to our customers over the API. So we don't just gobble up all of the Ultrafast capacity for ourselves.
What do you think is going to change in consumer behavior when they get AI at sort of this speed and this intelligence?
Yeah, it makes you sort of realize that before you were compromising and you had to switch tasks or you had to sort of like break the illusion that it wasn't really real time and it wasn't operating sort of like at the speed of thought. And with these kinds of speeds and future speeds, you know, this changes, right? You can stay in the loop, stay in the flow. Uh it becomes like this real-time interaction. You can get a lot more done much more quickly. And then you just sort of like every time you switch back to like, you know, normal speeds, you're like, 'Oh, wow.' You know, I forgot how slow this was.
Yeah. For those of you who remember, right, I mean, speed transformed the entertainment industry, right? We used to go to Blockbuster and then Netflix delivered in envelopes DVDs, right? But when the internet got fast, right, Netflix didn't become better at delivering DVDs. They became a movie studio, right? That speed enabled an entirely new creation of new markets. They ended up buying the movie studio, right? And I think speed when put in the hands of entrepreneurs and developers, it allows them to create and make new things, not just be faster at the things they've been building for a while.
Yeah, we're excited that it's on the API because it will enable different kinds of approaches, different types of products that we haven't yet come up with. Um but definitely when you're sort of like in the creation in the flow it feels like okay like now you can try something you know maybe draw something and then you know get like a few different proposals like in real time you know you can sort of explore the space different designs you can implement entire variations of backends like all in real time and it just feels like you know a new world is sort of like opening up.
One of the things I think OpenAI has been one of the many things that you guys have been absolutely best in the world at is sort of imagining the scale that AI could be. Right? You guys were out building Stargate when everybody thought it was laughably big and now everybody said we need it 10 times bigger. It's too small, right? Um tell us a little bit about how I mean educate us is how an organization thinks about scale in the way you guys do.
I think we just have deep conviction that and we had deep conviction that the models would get better and at some point you reach a point where you run the model on the GPU and it's providing more value than the cost of running it on the GPU and at that point you want to have all the capacity in the world to just like run it. Um and this will continue to be the case like we don't see a slowdown in the level of capabilities that we're able to develop with our models. Uh and so every time we have a new generation of models, we're like, well, you know, like the utility that we produce on the current compute that we have increases and it already vastly outpaces like the costs. And then this is like um this is a thing that we talk about all the time, right? So she's like, can we get more capacity? Can we get more capacity? I was like the demand.
They talk about it all the time. Believe him, right?
The demand fast outpaces you know the capacity that we have like across our entire fleet and I don't think there is a reason to believe that that will slow down or change.
All right. And last question you know we're just sort of in the first few months first six or eight months of a multi-year partnership and we announced sort of the frontier model at speed as a first look. Um as you look forward sort of what are you most excited about? I mean you've got maybe the best view of our industry of anybody.
Yeah. What I'm excited about is like it still feels extremely early. Uh and maybe going back to like why we were building all of this capacity and where why we continue to invest and we were early like if you look at the adoption of you know Chat is like above like a billion users right now but really the fraction of those that use those more sophisticated agentic workflows to help them day-to-day you know those are the numbers that we have published like 15 million that's like the number we published last week like we'll probably publish another number you know end of the week um even more impressive than that but it's growth growing fast, but you know, it's still like a very small fraction of that 1 billion. And so it feels extremely early. Um, and there's sort of like this um this thing right now is like you use it and you know, if you're technical, you're kind of like used to it being clunky, but the illusion is not quite perfect yet. Uh you don't yet have this personal AGI in your pocket that knows everything about you, your goals, your schedule for the week. Um you know like anything that is your tone of voice you're you know you preferred I mean every time I try to write something I was just like oh you know remember my tone of voice so you know like I have this somewhere in a file in a skill it's like that is clunky um so all of that is going to sort of reduce in something much more much simpler, safer as well and like readily understandable by everyone in the world and then that's going to be something quite different to what we, you know, have today.
What an exciting vision. Tibo, thank you so much for the partnership and for coming on stage and sharing. Ladies and gentlemen, Tibo.
Okay. Um, we have a little video of some what customers think both inside of OpenAI and elsewhere. Let's go.
This is the first time I felt like I was genuinely cheating at my job.
When our systems go down, it's like somebody hit the emergency stop on the assembly line. I'll tell it to monitor and pull that channel and essentially act as my eyes and ears to do investigation, to determine what the root cause is, to answer other people's questions as they join the channel.
There's an old adage that like performance can be a feature because when something becomes fast enough, it changes behavior. What used to take one to two hours to collect the data, sort through the data, normalize the data, and add context to it, rich intelligence to help me and my team make really effective decisions. Now, that happens within a 10 to 15 minute time frame. At times, it's near real time.
Using Ultrafast, I'm able to essentially search all of those haystacks all at the same time, which is really incredible, and do so concurrently. And now, frankly, Ultrafast is fast enough that I can usually get the same quality of results without splitting my attention.
Once I'm in flow and writing code, I feel like I have per unit of time, which is really the constraint, more intelligence on tap.
A quick codebase refactor costs almost nothing and it happens almost instantly. And this does mean that we build and ship things quicker and we can sustain this momentum for much longer as well because it's so quick and so effective.
You don't lose the quality of intelligence. We always want the most intelligence at the fastest speed. With 5.6 Ultrafast, we don't have to compromise on either.
Okay, let's race forward. Um, while we love our partners in the closed source community, we also are fastest on the full range of open-source models. All right, from large to small. Uh US non US on these like just about everything we do we're up to 15 times faster than the competition.
Now one of the things Tibo talked about and one of the things that's extremely sort of top of mind for us is the acquisition of data centers and we have data centers rolling in. Here's our data center in Santa Clara. Here's our data center in Toronto, Dallas, Texas, Minneapolis, Minnesota, Montreal. For those of you who are Canadian, I got the little thing over the E. Um, yeah, it was pointed out to me twice in presentations previously. Oklahoma City. And for those of you who don't know what they look like when you're building them, here's our data center in Alabama that's going up. Here's our data center in Leon, France going up. Here's our data center in a part of Norway that I can't pronounce. And here's our data center in Mikkeli, Finland, one of several that are going up. Right. We now have data centers across North America and Europe.
And we've brought on 600 megawatts, right? That's online or under contract for delivery by the end of next year. It's a huge effort and it's just the start. It's not nearly enough, but we have a whole team chasing this every single day.
Now, what you put in data centers, all right, is much more all right, than a fast AI accelerator, right? AI is generated by a cluster. All right? It's generated by a combination of equipment, some of ours and some of others. And what I'd like to do right now is invite onto stage someone who's sort of had an extraordinary career at Cisco. She was in my view singularly responsible for the rise of Cisco in the late 90s into one of the great companies of that era. She ran the Catalyst division which was Cisco's monster. All right. She's been CEO of Arista now for I think 14 or 15 years and has led that company to extraordinary success. Please join me in welcoming Jayshree Ullal to the stage.
So good to see you.
Congratulations Andrew. What a great company. What do you guys think? Cerebras.
Um, as we well first I began my career in networking competing against Jayshree and I would advise against that for those of you thinking about building switches do not compete with Jayshree that is some sort of unhappy stuff.
It's a lot more fun partnering.
It is fun partnering so look um one thing that's become clear as we sort of think about the AI landscape is the amount of equipment that goes into these clusters and the sort of the need for it to be coordinated and work together that if your network or your fabric can't deliver the goods, doesn't matter how fast your accelerator is, you're going to get bitten. And so sort of as we think about that um sort of how do you see Arista sort of working with Cerebras to deliver these sort of extraordinary solutions?
Absolutely. First of all I think you're only as good on the compute side as the network. Imagine if all your Cerebras stuff were idling and waiting for the network, right? So I'm hoping my network can pay for itself by making your compute much faster. We live in a world of, you know, you talked about 600 megawatts, you're going to go to gigawatts, terawatts. We live in a world of more and more thousands of tokens. Well, you know, we just have to deal with trillions of parameters. But you just can't do that single-handedly, you or me.
So it's really about building the best of breed stack together. And you know other companies would have you believe that everything's built by one. I think the power of all of us together is far greater than each one alone. So it starts at layer one. I'm creating my own OSI model here.
No no carry on. This is for those of us who learned the textbook that had seven layers. You know the physical power and cooling and infrastructure is powerful. I heard you talk about how you're running around trying to find this because compute power cooling is the scarcest commodity. Right. And if you can get that, you grab that and in any form and shape. And then of course there's the computer accelerators. And nobody does this better than Cerebras from an inference perspective. Of course there's a few others we all know about that do some training. But here I'm going to focus on the inference. But to make this all hum, we've had to really think about what we do differently with AI than we did in the past with cloud. In cloud it was easy to just say okay we'll just throw a bunch of capacity do low latency have lots of bandwidth and you know deal with one set of traffic which was cloud computing. In AI you really have different forms of traffic the fidelity is different the type of traffic is different the any is different so we had to build different types of traffic to deal with networks to deal with different classes of frontier models and application agents and I think this whole area especially of frontier models and agent is evolving too because the enterprise has hardly come in yet, right? Um so.
That's true. I mean that's a really important point that the enterprise has been on the sideline to date.
Yeah. So we mostly talk about the neoclouds and the hyperscalers etc. But I believe when you're here next time you're going to be talking about a lot more agents agentic generative AI even going into our phones and this is going to be powerful and behind all of that is of course the importance of a network.
I have no questions. Let's keep going.
Oh, you don't. Okay. So, I like, you know, given our engineering backgrounds, I like to think in terms of X and Y and Z axes, right? And frankly, as a student, I was terrible at the third dimension, but it's important. So, when we think of this and how we work with Cerebras accelerators, we think scale up, how do we connect to as many of your I know you don't build postage stamps, you only build dinner plates, right? So as much of that to get the greatest radix whether it's 64 or 128 how much of that can we connect until we run out of compute capacity and network capacity and then we go from scale up to scale out how do we connect these racks together and this is where we come in and I think we've worked very closely together but there's another emerging market which is how do you distribute the compute you're never going to get enough just to be in within one surface area and this is where you can have a multi-tenant capacity multi-tenant engineering where you can secure different connections of compute in a distributed fashion across distance and this is what we call the scale across that goes across locations.
So that's a little tutorial in networking from me.
Well look I think as we sort of have grown our partnership Jayshree supported us when we weren't buying very much at all and now we're buying a fair bit. Um we appreciate it we want to thank you. Um, and maybe by way of final question, what do you see in the next two or three years for the networking industry and for the sort of demands placed on it by this new type of compute?
