Everything Announced at the Nvidia Live CES 2026 Event in 9 Minutes

[music] Hello, Las Vegas. Today, we're

announcing Alpamo, the world's first thinking reasoning autonomous vehicle AI. Alpo is

trained end to end, literally from camera in to actuation out. the camera

in lots and lots of miles that are driven by itself where we human drive it dri using human demonstration and we have lots and lots of miles that

are generated by cosmos. In addition to that hundreds of thousands of examples are labeled very very carefully so that we could teach the car how to drive.

Alpha Mayo does something that's really special. Not only does it take sensor

special. Not only does it take sensor input and activates steering wheel, brakes and and acceleration, it also

reasons about what action it is about to take. It tells you what action it's

take. It tells you what action it's going to take, the reasons by which it came about that action and then of course the trajectory.

All of these are coupled directly and trained very specifically by a large combination of human trained and as well as cosmos generated data and so but

every single car will have autonomous vehicle capability. Every single car

vehicle capability. Every single car will be AI powered and so the the model layer in this case is Alpha Mayo and the application above that is the Mercedes-Benz.

Okay. And so, so this entire stack is our first Nvidia first entire stack endeavor and we've been working on it for this entire time and I'm just so

happy that the first AV car from Nvidia is going to be on the road in Q1 and then it goes Europe in Q2 here in the United States in Q1 then Europe in Q2

and I think it's Asia in Q3 and Q4. And

the powerful thing is that we're going to keep on updating it with next ver next versions of Alpamo and versions after that. This car just got rated. It

after that. This car just got rated. It

just went to production. The

Mercedes-Benz CLA was just rated by NCAAP, the world's safest car.

[applause] It is the only system that I know that has every single line of code, the chip, the system, every line of code safety

certified. The entire model system is

certified. The entire model system is based on a sensors are diverse and redundant and so is the self-driving car stack. The Alphao stack is trained end

stack. The Alphao stack is trained end to end and has incredible skills.

However, nobody knows until you drive it forever that it's going to be perfectly safe.

And so that we the way we guard rail that is with another software stack, an entire AV stack underneath. That entire

AV stack is built to be fully traceable and it's taken us some five years to build that some six, seven years actually to build that second stack.

These two software stacks are mirroring each other. And then we have a policy

each other. And then we have a policy and safety evaluator decide is this something that I'm very confident and can reason about driving very safely. If

so, I'm going to have Alpamo do it. If

it's a circumstance that I'm not very confident in and the safety um policy evaluator decide that we're going to go back to a a very a simpler, safer guard rail system, then it goes back to the

classical AV stack. We're the only car in the world with both of these AV stacks running. And all safety systems

stacks running. And all safety systems should have diversity and redundancy.

Hurry up. I got a lot of stuff to cover.

Come on, hurry.

Did you tell R2-D2 you're going to be here?

Did you? And C3PO. And today we're announcing that Seammens is also doing the same thing. We're going to integrate CUDA X, physical AI, agentic AI, Nemo,

Neotron deeply integrated into the world of Seammens. And the reason for that is

of Seammens. And the reason for that is this. First, we designed the chips

this. First, we designed the chips and all of it in the future will be accelerated by Nvidia. You're going to be very happy about that. We're going to have agent chip designers and system

designers working with us, helping us do design just as we have agentic software engineers helping our software engineers code today. And so we'll have agentic

code today. And so we'll have agentic chip designers and system designers.

We're going to create you inside this.

But then we have to build you. We have

to build the plants, the factories that make manufacture you. We have to design the manufacturing lines that assemble all of you. And these manufacturing

plants are going to be essentially gigantic robots. Incredible. Isn't that

gigantic robots. Incredible. Isn't that

right?

I know. I know. And so Vera Rubin is the person that we named our next computer after. Isn't that a good idea?

after. Isn't that a good idea?

I know.

Okay. Okay, Vera Rubin is designed to address this fundamental challenge that we have. The amount of computation

we have. The amount of computation necessary for AI is skyrocketing and if Vera Rubin is going to be in time

for this year, it must be in production by now. And so today I can tell you that

by now. And so today I can tell you that Vera Rubin is in full production. This

is a Reuben pod. 1152 GPUs

in 16 racks. Each one of the racks as you know has uh 72

Vera Rubin or 72 Reubins. Each one of the Reubins is two actual GPU dies connected together. I'm going to show

connected together. I'm going to show I'm going to show it to you. The Vera

CPU. I'm so proud of it. In a power constrained world, Gray CPU is two times the performance. In a power constrained

the performance. In a power constrained world, it's twice the performance per watt of the world's most advanced CPUs.

Its data rate is insane. It was designed to process supercomputers and Vera was an incredible GPU. Grace was an

incredible GPU. Now Vera increases the

incredible GPU. Now Vera increases the singlethreaded performance, increases the capacity of the memory, increases everything just dramatically. It's a

giant chunk. This is the Vera CPU.

This is one CPU and this is connected to the Reuben GPU. Look at that thing.

It's a giant chip. We now have a new processor called Bluefield 4. Bluefield

4 allows us to take a large large very large data center, isolate different parts of it so that different users could use different parts of it. Make

sure that everything could be virtualized if they decide to be virtualized. So you offload a lot of the

virtualized. So you offload a lot of the um virtualization software, the security software, the networking software for your north south traffic. And so

Bluefield 4 comes standard with every single one of these compute nodes. We

decided that we would create an industry standard system so that the entire ecosystem all of our supply chain could standardize on these components. There

some 80,000 different components that make up this these MGX systems and it's a total waste if it were to change it every single

year. every single major computer

year. every single major computer company from Foxcon to Quanta to Wistron, you know, the list goes on and on and on to HP and Dell and Lenovo, everybody knows how to build these

systems. And so the fact that we could squeeze Ruben, Vera Rubin into this even though the performance is so much so much higher and very importantly the

power is twice as high. The power of Vera Rubin is twice as high as Grace Blackwell. And yet, and this is the

Blackwell. And yet, and this is the miracle, the air that goes into it, the the air flow is about the same. And very

importantly, the water that goes into it is the same temperature, 45° C. With 45°

C, no water chillers are necessary for data centers. We're basically cooling

data centers. We're basically cooling this supercomput with hot water.

This is this is the world's first manufacturing chip using uh TSMC's new process that we co-inovated called coupe. It's a silicon photonix

coupe. It's a silicon photonix integrated silicon photonix process technology. And this allows us to take

technology. And this allows us to take silicon photonix directly right to the chip. And this is 512 ports at 200

chip. And this is 512 ports at 200 gigabits per second.