Nvidia CEO Jensen Huang gives a keynote address at the GTC conference in Washington — 10/28/25

Welcome to the stage, Nvidia founder and

CEO, Jensen Wong.

[Applause]

Washington DC.

Washington DC. Welcome to GTC.

[Music]

It's hard not to be sentimental and

proud of America. I got to tell you

that. Was that video amazing?

Thank you.

Nvidia's creative team does an amazing

job. Welcome to GTC. We have a lot to

cover with you today. Um GTC is where we

talk about industry,

science,

computing,

the present, and the future. So, I've

got a lot to cover with you today, but

before I start, I want to thank all of

our partners who helped sponsor this

great event. You'll see all of them

around the show. They're here to meet

with you and uh uh really great. We

couldn't do what we do without all of

our ecosystem partners. Uh this is the

Super Bowl of AI, people say. And

therefore, every Super Bowl should have

an amazing pregame show. What do you

guys think about the pregame show? and

our all allstar allstar athletes and

allstar cast. Look at these guys.

Somehow I turned out the buffest. What

do you guys think?

I don't know if I had something to do

with that.

Nvidia invented a new computing model

for the first time in 60 years. As you

saw in the video, a new computing model

rarely comes about. It takes an enormous

amount of time and set of conditions. We

observed, we invented this computing

model because we wanted to solve

problems that generalpurpose computers,

normal computers could not. We also

observed that someday transistors will

continue. The number of transistors will

grow, but the performance and the power

of transistors

will slow down. that Moore's law will

not continue beyond limited by the laws

of physics and that moment has now

arrived. Dinard scaling has stopped.

It's called dinard scaling. Dard scaling

has stopped nearly a decade ago and in

fact the transistor performance and its

power associated has slowed tremendously

and yet the number of transistor

continued. We made this observation a

long time ago and for 30 years we've

been advancing this form of computing we

call accelerated computing. We invented

the GPU. We invented the the programming

model called CUDA. And we observed that

if we could add a processor that takes

advantage of more and more and more

transistors, apply parallel computing,

add that to a sequential processing CPU

that we could extend the capabilities of

computing well beyond well beyond. And

that moment has really come. We have now

seen that inflection point. Accelerated

computing its moment has now arrived.

However, accelerated computing is a

fundamentally different programming

model. You can't just take a CPU

software software written by hand

executing sequentially and put it onto a

GPU and have it run properly. In fact,

if you just did that, it actually runs

slower. And so, you have to reinvent new

algorithms. You have to create new

libraries. You have to in fact rewrite

the application which is the reason why

it's taken so long. It's taken us nearly

30 years to get here. But we did it one

domain at a time.

This is the treasure of our company.

Most people talk about the GPU. The GPU

is important, but without a programming

model that sits on top of it, and

without dedication to that programming

model, keeping it compatible over

generations, we're now CUDA 13 coming up

with CUDA 14,

hundreds of millions of GPUs running in

every single computer, perfectly

compatible. If we didn't do that, then

developers wouldn't target this

computing platform. If we didn't create

these libraries, then developers

wouldn't know how to use the algorithm

and use the architecture to its fullest.

One application after another. I mean,

these this is really the this is really

the treasure of our company. CU litho

computational lithography.

It took us nearly seven years to get

here with KU Litho and now TSMC uses it,

Samsung uses, ASML uses it. This is an

incredible library for computational

lithography. the first step of making a

chip. Sparse solvers for CAE

applications.

Co-op, a numerical optimization is

broken just about every single record.

The traveling salesperson problem, how

to connect millions of products with

millions of customers in the supply

chain. Warp Python solver for CUDA for

simulation. QDF a dataf frame approach

basically accelerating

SQL dataf frame pro dataf frame

databases. Um this library is the one

that started AI alto together coupnn

the the library on top of it called

megatron core made it possible for us to

simulate and train extremely large

language models. The list goes on. Uh,

Monai, really, really important, the

number one medical imaging

AI framework in the world. Uh, by the

way, we're not going to talk a lot about

health care today, but be sure to see

Kimberly's keynote. She's going to talk

a lot about the work that we do in

healthcare. And the list goes on. Uh,

genomics processing, Ariel, pay

attention. We're going to do something

really important here today. Um, coup

quantum quantum computing. This is just

a representative of 350 different

libraries in our company. And each one

of these libraries redesigned the

algorithm necessary for accelerated

computing. Each one of these libraries

made it possible for all of the

ecosystem partners to take advantage of

accelerated computing. And each one of

these libraries opened new markets for

us. Let's take a look at what CUDA X can

do.

Ready go.

[Music]

Heat. Heat.

[Music]

Heat. Heat.

[Music]

[Applause]

[Music]

Heat.

[Music]

[Music]

Heat.

[Music]

Is that amazing?

Every

everything you saw was a simulation.

There was no art, no animation. This is

the beauty of mathematics. This is deep

computer science, deep math. And it's

just incredible how beautiful it is.

Every industry was covered from

healthcare and life sciences to

manufacturing robotics autonomous

vehicles, computer graphics, even video

games. That first shot that you saw was

the first application Nvidia ever ran.

And that's where we started in 1993. And

we kept believing in what we were trying

to do. And it took, it's hard to imagine

that you could see that first virtual

fighter scene come alive. And that same

company believed that we would be here

today. It's just a really, really

incredible journey. I want to thank all

the NVIDIA employees for everything that

you've done. It's really incredible.

We have a lot of industries to cover

today. I'm going to cover AI,

6G,

quantum

models,

enterprise computing, robotics, and

factories. Let's get started. We have a

lot to cover, a lot of big announcements

to make, a lot of new partners that

would very much surprise you.

Telecommunications

is the backbone, the lifeblood of our

economy, our industries, our national

security. And yet,

ever since the beginning of wireless

where we define the technology, we

defined the global standards. We

exported American technology all around

the world so that the world can build

on top of American technology and

standards.

It has been a long time since that's

happened. Wireless technology around the

world largely today deployed on foreign

technologies.

Our fundamental communication fabric

built on foreign technologies.

That has to stop and we have an

opportunity to do that especially during

this fundamental platform shift. As you

know, computer technology is at the

foundation of literally every single

industry. It is the single most

important instrument of science. It's

the single most important instrument of

industry.

And I just said we're going through a

platform shift. That platform shift

should be the once-ina-lifetime

opportunity for us to get back into the

game for us

to start innovating with American

technology. today. Today we're

announcing that we're going to do that.

We have a big partnership with Nokia.

Nokia is the second largest

telecommunications maker in the world.

It's a $3 trillion industry.

Infrastructure is hundreds of billions

of dollars. There are millions of base

stations around the world.

If we could partner, we could build on

top of this incredible new technology

fundamentally based on accelerated

computing and AI and for United States

for America to be at the center of the

next revolution in 6G. So today we're

announcing that Nvidia has a new product

line. It's called the NVIDIA Arc, the

Aerial Radio Network Computer.

Aerial RAM computer ARC. ARC is built

from three fundamental new technologies.

The gray CPU, the Blackwell GPU, and our

ConnectX Melanox Connect X networking

designed for this application. And all

of that makes it possible for us to run

this library, this CUDA X library that I

mentioned earlier called Aerial. Aerial

is essentially

a wireless

communication system running on top of

CUDA X. We're gonna we're going to

create for the first time a

softwaredefined

programmable computer that's able to

communicate wirelessly and do AI

processing at the same time. This is

completely revolutionary. We call it

Nvidia Arc and Nokia

is going to work with us to integrate

our technology, rewrite their stack.

This is a company with 7,000 fundamental

essential 5G patents.

Hard to imagine any greater leader in

telecommunications. So, we're going to

partner with Nokia. They're going to

make Nvidia Arc their future base

station. NVIDIA Arc is also compatible

with Airscale, the current Nokia base

stations. So, what that means is we're

going to take this new technology and

we'll be able to upgrade millions of

base stations around the world with 6G

and AI. Now 6G and AI is really quite

fundamental in the sense that for the

first time we'll be able to use AI

technology

AI for RAN to make radio communications

more spectral efficient

doing using artificial intelligence

reinforcement learning adjusting the

beam forming in real time in context

depending on the surroundings and the

traffic and the mobility the weather all

of that could be taken into account so

that we could improve spectral

efficiency. Spectral efficiency consumes

about 1 and a half to 2% of the world's

power. So improving spectral efficiency

not only improves the amount of data we

can put through wireless networks

without increasing the amount of energy

necessary. The other thing that we could

do

AI for RAN is AI on RAM. This is a brand

new opportunity. Remember the internet

enabled communications but amazingly

smart companies AWS built a cloud

computing system on top of the internet.

We are now going to do the same thing on

top of the wireless telecommunications

network. This new cloud will be an edge

industrial robotics cloud. This is where

AI on RAN the first is AI for RAN to

improve radio radio spectrum efficiency.

The second is AI on essentially cloud

computing for wireless

telecommunications.

Cloud computing will be able to go right

out to the edge where data centers are

not are not because we have base

stations all over the world. This

announcement is really exciting. Justin

Hodar the CEO I think he's somewhere in

the room. Thank you for your

partnership. Thank you for helping

United States bring telecommunication

technology back to America. This is

really a fantastic, fantastic

partnership. Thank you very much.

That's the best way to celebrate Nokia.

Let's talk about quantum computing.

1981

particle physicist quantum physicist

Richard Feman imagined a new type of

computer that can simulate nature

directly. To simulate nature directly

because nature is quantum. He called it

a quantum computer. 40 years later the

industry has made a fundamental

breakthrough. 40 years later, just last

year, a fundamental breakthrough. It is

now possible to make one

logical cubit. One logical cubit. One

logical cubit that's coherent, stable,

and error corrected. In the past, now

that one logical cubit consists of could

be sometimes tens, sometimes hundreds of

physical cubits all working together. As

you know, cubits, these particles are

incredibly fragile.

They could be unstable very easily. Any

observation, any sampling of it, any

environmental condition causes it to

become decoherent. And so, it takes

extraordinarily well-controlled

environments. And now also a lot of

different physical cubits for them to

work together and for us to do error

correction on these what are called

auxiliary or syndrome cubits for us to

error correct them and infer what that

logical cubit state is.

There are all kinds of different types

of quantum computers. superconducting,

photonic, trapped ion, stable atom, all

kinds of different ways to create a

quantum computer. Well, we now realize

that it's essential for us to connect a

quantum computer directly to a GPU

supercomputer so that we could do the

error correction so that we could do the

artificial intelligence calibration and

control of the quantum computer and so

that we could do simulations

collectively working together. the right

algorithms running on the GPUs, the

right algorithms running on the QPUs and

the two processors, the two computers

working side by side. This is the future

of quantum computing. Let's take a look.

There are many ways to build a quantum

computer.

Each uses cubits, quantum bits as its

core building block. But no matter the

method, all cubits, whether

superconducting cubits, trapped ions,

neutral atoms, or photons, share the

same challenge. They're fragile and

extremely sensitive to noise. Today's

CQITS remain stable for only a few

hundred operations. But solving

meaningful problems requires trillions

of operations. The answer is quantum

error correction. Measuring disturbs a

cubit which destroys the information

inside it. The trick is to add extra

cubits entangle so that measuring them

gives us enough information to calculate

where errors occurred without damaging

the cubits we care about. It's brilliant

but needs beyond state-of-the-art

conventional compute.

That's why we built NVQ Link, a new

interconnect architecture that directly

connects quantum processors with NVIDIA

GPUs.

Quantum error correction requires

reading out information from Qbits,

calculating where errors occur and

sending data back to correct them. MVQL

link is capable of moving terabytes of

data to and from quantum hardware, the

thousands of times every second needed

for quantum error correction.

At its heart is CUDAQ, our open platform

for quantum GPU computing. Using MVQLink

and CUDAQ, researchers will be able to

do more than just error correction. They

will also be able to orchestrate quantum

devices and AI supercomputers to run

quantum GPU applications.

Quantum computing won't replace

classical systems. They will work

together fused into one accelerated

quantum supercomputing platform.

Wow, this is a really long stage.

You know, CEOs, we don't just sit at our

desk typing. It's this is a physically

job. Physical job. So, so today we're

announcing the NV MVQ link. MVQL link

and it's made possible by two things. Of

course, this interconnect that does

quantum computer control and

calibration,

quantum error correction, as well as

connects two computers, the QPU and our

GPU supercomputers to do hybrid

simulations.

It is also completely scalable. It

doesn't just do the error correction for

today's number of few cubits. It does

error correction for tomorrow where

we're going to essentially scale up

these quantum computers from the

hundreds of cubits we have today to tens

of thousands of cubits, hundreds of

thousands of cubits in the future. So we

now have an architecture that can do

control, co- simulation, quantum error

correction and scale into that future.

The industry support has been incredible

between the invention of CUDA Q.

Remember CUDA was designed for GPU CPU

accelerated computing. Basically using

both processors to do use the right tool

to do the right job. Now CUDA Q has been

extended beyond CUDA so that we could

support QPU and have the two processors

QPU and the GPU work and have

computation move back and forth within

just a few microsconds. The essential

latency to be able to cooperate with the

quantum computer. So now CUDAQ is such

an incredible breakthrough adopted by so

many different developers. We are

announcing today 17 different quantum

computer industry companies supporting

the MVQ link and and I'm so excited

about this eight different DOE labs

Berkeley Brook Haven Fermy Labs in

Chicago Lincoln Laboratory Los Alamos

Oakidge Pacific Northwest San Diego

National Lab just about every single DOE

lab has engaged us working with our

ecosystem of quantum computer companies

and these quantum controllers so that we

could integrate quantum computing in

into the future of science.

Well, I have one more additional

announcement to make. Today, we're

announcing that the Department of Energy

is partnering with Nvidia to build seven

new AI supercomputers to advance our

nation's science.

I have to have a shout out for Secretary

Chris Wright. He has brought so much

energy to the DOE. A surge of energy, a

surge of passion to make sure that

America leads science. Again, as I

mentioned, computing is the fundamental

instrument of science. And we are going

through several platform shifts. On the

one hand, we're going to accelerated

computing. That's why every future

supercomputer will be GPUbased

supercomputer.

We're going to AI. So that AI and

principled solvers, principal

simulation,

principal physics simulation is not

going to go away, but it could be

augmented enhanced scaled use

surrogate models, AI models working

together. We also know that principal

solvers, classical computing could be

enhanced to understand the state of

nature using quantum computing. We also

know that in the future we have so much

signal, so much data we have to sample

from the world. Remote sensing is more

important than ever. And these

laboratories are impossible to

experiment at the scale and speed we

need to unless they're robotic

factories, robotic laboratories.

So all of these different technologies

are coming into science at exactly the

same time. Secretary Wright understands

this and he wants the DOE to take this

opportunity to supercharge themselves

and make sure the United States stay at

the forefront of science. I want to

thank all of you for that. Thank you.

Let's talk about AI.

What is AI? Most people would say that

AI is a chatbot and it it's rightfully

so. There's no question that Chad GPT is

at the forefront of what people would

consider AI. However, just as you see

right now, these scientific

supercomputers are not going to run

chatbots. They're going to do basic

science. Science, AI, the world of AI is

much much more than a chatbot. Of

course, the chatbot is extremely

important and AGI is fundamentally

critical. deep computer science,

incredible computing, great

breakthroughs are still essential for

AGI. But beyond that, AI is a lot more.

AI is in fact, I'm going to describe AI

in a couple different ways. This first

way, the first way you think about AI is

that it has completely reinvented the

computing stack.

The way we used to do software was hand

coding. Hand coding software running on

CPUs.

Today, AI is machine learning training,

data intensive programming, if you will,

trained and learned by AI that runs on a

GPU. In order to make that happen, the

entire computing stack has changed.

Notice you don't see Windows up here.

You don't see CPU up here. You see a

whole different a whole fundamentally

different stack. Everything from the

need for energy and this is another area

where our administration, President

Trump gets deserves enormous credit, his

pro-en energy initiative, his

recognition that this industry needs

energy to grow. It needs energy to

advance and we need energy to win. His

recognition of that and putting the

weight of the nation behind pro- energy

growth completely changed the game. If

this didn't happen, we could have been

in a bad situation. And I want to thank

President Trump for that.

On top of energy are these GPUs. And

these GPUs are connected into built into

infrastructure that I'll show you later.

On top of this infrastructure which in

cons consists of giant data centers like

easily many times the size size of this

room enormous amount of energy which

then transfer transforms the energy

through this new machine called GPU

supercomputers to generate numbers.

These numbers are called tokens.

the language, if you will, the

computational unit, the vocabulary of

artificial intelligence. You can

tokenize almost anything. You can

tokenize, of course, the English word.

You can tokenize images. That's the

reason why you're able to recognize

images or generate images, tokenize

video, tokenize 3D structures. You could

tok to tokenize chemicals and proteins

and genes. You could tokenize cells,

tokenize almost anything with structure,

anything with information content.

Once you could tokenize it, AI can learn

that language and the meaning of it.

Once it learns the meaning of that

language, it can translate. It can

respond just like you respond just like

you interact with chat GBT. And it could

generate just as chat GBD can generate.

So all of the fundamental things that

you see Chad GPD do, all you have to do

is imagine what if it was a protein,

what if it was a chemical, what if it

was a 3D structure like a factory, what

if it was a robot and the token was

understanding behavior

and tokenizing motion and action. All of

those concepts are basically the same,

which is the reason why AI is making

such extraordinary progress. And on top

of these models are applications

transformers.

Transformers is not a universal model.

It's incredibly effective model. But

there's no one universal model. It's

just that AI has universal impact. There

are so many different types of models.

There's in the last several years we

enjoyed the invention and went through

the innovation breakthroughs of

multimodality.

There's so many different types of

models. There's CNN models, competition

neuronet network models, their state

space models, the graph neuronet network

models, multimodal models, of course,

all the different tokenizations and

token methods that I just described. You

could have models that are spatial in

its understanding, optimized for spatial

awareness. You could have models that

are optimized for long sequence,

recognizing subtle information over a

long period of time. There's so many

different types of models.

On top of these models architectures, on

top of these model architectures are

applications,

the software of the past. And this is a

a profound understanding, a profound

observation of artificial intelligence

that the software industry of the past

was about creating tools. Excel is a

tool.

Word is a tool. A web browser is a tool.

The reason why I know these are tools is

because you use them. The tools

industry, just as screwdrivers and

hammers, the tools industry is only so

large. In the case of IT tools, they

could be database tools. These IT tools

is about a trillion dollars or so. But

AI is not a tool.

AI is work.

That is the profound difference. AI is

in fact workers that can actually use

tools. One of the things I'm really

excited about is the work that Irvin's

doing at Perplexity. Perplexity using

web browsers to book vacations or do

shopping. Basically an AI using tools.

Cursor is an AI, a nigantic AI system

that we use at NVIDIA. Every single

software engineer at Nvidia uses Cursor.

That's improved our productivity

tremendously. It's basically a partner

for every one of our software engineers

to generate code and it uses a tool and

the tool it uses is called VS code. So

cursor is an AI agentic AI system that

uses VS code. Well, all of these

different industries whether it's chat

bots or digital biology where we have AI

assistant researchers or what is a robo

taxi

inside a robo taxi? Of course, it's

invisible, but obviously there's a

there's a AI chauffeur. That chauffeur

is doing work. And the tool that it uses

to do that work is the car. And so

everything that we've made up until now,

the whole world, everything that we've

made up until now are tools. Tools for

us to use. For the very first time,

technology is now able to do work and

help us be more productive. The list of

opportunities go on and on, which is the

reason why AI addresses

the segment of the economy that it has

never addressed. It is a few trillion

dollars that sits underneath the tools

of a hundred trillion dollar global

economy. Now, for the first time, AI is

going to engage that hundred trillion

dollar economy and make it more

productive, make it grow faster, make it

larger. We have a severe shortage of

labor. Having AI that augments labor is

going to help us grow. Now what's

interesting about this from a technology

industry perspective also is that in

addition to the fact that AI is new

technology that addresses new segments

of the economy AI in itself is also a

new industry

this token as I was explaining earlier

these numbers

after you tokenize all these different

modalities of information there's a

factory that needs to produce these

numbers unlike the computer industry and

the chip industry of the past.

Notice if you look at the chip industry

of the past, the chip industry

represents about 5 to 10%

maybe less 5% or so of a multi- trillion

dollar few trillion dollar IT industry.

And the reason for that is because it

doesn't take that much computation to

use Excel. It doesn't take that much

computation to use browsers. Doesn't

take that much computation to use Word.

We do the computation. But in this new

world,

there needs to be a computer that

understands context all the time. It

can't precomputee that because every

time you use the computer for AI, every

time you ask the AI to do something, the

context is different. So, it has to

process all of that information.

Environmental, for example, in the case

of a self-driving car, it has to process

the context of the car. context

processing. What is the instruction

you're asking the AI to do? Then it's

got to go and break down the problem

step by step, reason about it, and come

up with a plan and execute it. Every

single one of that step requires

enormous number of tokens to be

generated which is the reason why we

need a new type of system and I call it

an AI factory. It's an AI factory for

short. It's unlike a data center of the

past. is an AI factory because

this factory produces one thing unlike

the data centers of the past that does

everything. Stores files for all of us,

runs all kinds of different

applications. You could use that data

center like you can use your computer

for all kinds of applications. You could

use it to play a game one day. You could

use it to browse the web. You could use

it, you know, to do your accounting. And

so that is a computer of the past, a

universal generalpurpose computer.

The computer I'm talking about here is a

factory. It runs basically one thing. It

runs AI and its purpose, its purpose is

designed to produce tokens that are as

valuable as possible, meaning they have

to be smart. And you want to produce

these tokens at incredible rates because

when you ask an AI for something, you

would like it to respond. And notice

during peak hours, these AIs are now

responding slower and slower because

it's got a lot of work to do for a lot

of people. And so you wanted to produce

valuable tokens at incredible rates and

you wanted to produce it cost

effectively. Every single word that I

used are consistent with an AI factory,

with a car factory or any factory. It is

absolutely a factory. And these

factories, these factories never existed

before. And inside these factories are

mountains and mountains of chips.

Which brings

to today.

What happened in the last couple years?

And in fact, what happened this last

year? Something fairly profound happened

this year. Actually, if you look in the

beginning of the year, everybody has

some attitude about AI. That attitude is

generally this is going to be big. It's

going to be the future. And somehow a

few months ago, it kicked into

turbocharge. And the reason for that is

several things.

The first is that we in the last couple

years have figured out how to make AI

much much smarter.

Rather than just pre-training,

pre-training basically says let's take

all of the all of the information that

humans have ever created. Let's give it

to the AI to learn from. It's

essentially memorization and

generalization.

It's no it's not unlike going to school

back when we were kids. the first stage

of learning. Pre-training was never

meant just as preschool

was never meant to be the end of

education. Pre-training, preschool was

simply teaching you the basic skills of

intelligence so that you can understand

how to learn everything else. Without

vocabulary, without understanding of

language and how to communicate, how to

think, it's impossible to learn

everything else. The next is post

training. Post-training after

pre-training is teaching you skills.

Skills to solve problem, break down

problems, reason about it, how to solve

math problems, how to code, how to think

about these problems step by step, use

first principle reasoning. And then

after that

is where computation really kicks in. As

you know, for many of us, you know, we

went to school and that's in my case

decades ago, but ever since I've learned

more, thought about more, and the reason

for that is because we're constantly

grounding oursel in new knowledge. We're

constantly doing research, and we're

constantly thinking. Thinking is really

what intelligence is all about. And so

now we have three fundamental technology

skills. We have these three technologies

pre-training which still requires

enormous enormous amount of computation.

We now have post-training which uses

even more computation. And now thinking

puts incredible amounts of computation

load on the infrastructure because it's

thinking on our behalf for every single

human. So the amount of computation

necessary for AI to think inference is

really quite extraordinary. Now I used

to hear people say that inference is

easy. Nvidia should do training. Nvidia

is going to do you know they are really

good at this so they're going to do

training. That inference was easy. How

could thinking be easy? Regurgitating

memorized content is easy. Regurgitating

the multiplication tables easy. Thinking

is hard. which is the reason why these

three scales these three new scaling

laws which is all of it in in full steam

has put so much pressure on the amount

of computation. Now another thing has

happened from these three scaling laws.

We get smarter models and these smarter

models need more compute. But when you

get smarter models, you get more

intelligence.

People use it

as if anything happens. I want to be the

first one out.

Jazz. I'm sure it's fine. Probably just

lunch. My stomach.

Was that me?

And so, so where was I? The smarter your

models are, the smarter your models are,

the more people use it. It's now more

grounded. It's able to reason. It's able

to solve problems it never learn how to

solve before because it could do

research. Go learn about it. come back,

break it down, reason about how to solve

your how to answer your question, how to

solve your problem, and go off and solve

it. The amount of thinking is making the

models more intelligent. The more

intelligent it is, the more people use

it. The more intelligent it is, the more

computation is necessary. But here's

what happened.

This last year,

the AI industry turned the corner.

Meaning that the AI models are now smart

enough. They're making they're worthy.

They're worthy to pay for. Nvidia pays

for every license of Cursor. And we

gladly do it. We gladly do it because

Curser is helping a several hundred,000

employee software engineer or AI

researcher be many, many times more

productive. So, of course, we'd be more

than happy to do that. These AI models

have become good enough that they are

worthy to be paid for. Cursor, 11 Labs,

Syntheasia, A Bridge, Open Evidence, the

list goes on. Of course, open AI, of

course, Claude. These models are now so

good that people are paying for it. And

because people are paying for it and

using more of it, and every time they

use more of it, you need more compute.

We now have two exponentials.

These two exponentials, one is the

exponential compute requirement of the

three scaling law. And the second

exponential, the more pe the smarter it

is, the more people use it, the more

people use it, the more computing it

needs. Two exponentials now putting

pressure on the world's computational

resource

at exactly the time when I told you

earlier that Moore's law has largely

ended. And so the question is what do we

do? If we have these two exponential

demands growing and if we don't if we

don't find a way to drive the cost down

then this positive feedback system this

circular feedback system essentially

called the virtual cycle essential for

almost any industry

essential for any platform industry. It

was essential for Nvidia. We have now

reached the virtual cycle of CUDA.

The more applications, the more the more

applications people create, the more

valuable CUDA is. The more valuable CUDA

is, the more CUDA computers are

purchased. The more CUDA computers are

purchased, more developers want to

create applications for it. That virtual

cycle

for Nvidia has now been achieved after

30 years. We have achieved that also. 15

years later, we've achieved that for AI.

AI has now reached the virtual cycle.

And so the more you use it because the

AI is smart and we pay for it. The more

profit is generated, the more profit

generated, the more computes put to on

the on the grid, the more compute is put

into AI factories, the more comput the

AI becomes smarter, the smarter, more

more people use it, more applications

use it, the more problems we can solve.

This virtual cycle is now spinning. What

we need to do is drive the cost down

tremendously so that one the user

experience is better when you prompt the

AI it responds to you much faster and

two so that we keep this virtual cycle

going by driving its cost down so that

it could get smarter so that more people

use it so that so on so forth that

virtual cycle is now spinning but how do

we do that when Moore's law has really

reached its limit well the answer is

called co-design design.

You can't just design chips and hope

that things on on top of it is going to

go faster. The best you could do in

designing chips is add I don't know 50%

more transistors every couple of years

and if you added more transistors just

you know we can add more transistors and

TSMC's got a lot of transistor

incredible company. We just keep adding

more transistors. However, that's all in

percentages not exponentials.

We need to compound exponentials to keep

this virtual cycle going. We call it

extreme code design. Nvidia is the only

company in the world today that

literally starts from a blank sheet of

paper and can think about new

fundamental architecture, computer

architecture, new chips, new systems,

new software, new model architecture,

and new applications all at the same

time. So many of the people in this room

are here because you're different parts

of that layer that different parts of

that stack and working with Nvidia.

We fundamentally rearchitect everything

from the ground up. And then because AI

is such a large problem, we scale it up.

We created a whole computer, a computer

for the first time that has scaled up

into an entire rack. That's one

computer, one GPU. And then we scale it

out by inventing a new AI Ethernet

technology we call Spectrum X Ethernet.

Everybody will say Ethernet is Ethernet.

Ethernet is hardly Ethernet. Ethernet.

Spectrum X Ethernet is designed for AI

performance. And it's the reason why

it's so successful. And even that's not

big enough. We'll fill this entire room

of AI supercomputers and GPUs.

That's still not big enough because the

number of applications and the number of

users for AI is continuing to grow

exponentially. And we connect multiple

of these data centers together and we

call that scale across spectrum XGS

gigascale X spectrum X gigascale XGS. By

doing so, we do code design at such a

such an enormous level, such an extreme

level that the performance benefits are

shocking. Not 50% better each

generation, not 25% better each

generation, but much much more. This is

the most extreme code-designed computer

we've ever made and quite frankly made

in modern times. Since the IBM system

360, I don't think a computer has been

ground up, reinvented like this ever.

This system was incredibly hard to

create. I'll show you the benefits in

just a second. But essentially what

we've done, essentially what we've done,

we've created otherwise,

hey Janine, you can come out. It's

you have to have to meet me like

halfway.

All right. So, this is kind of like

Captain America shield.

So, Envy Link 72, Envink 72,

if we were to create one giant chip, one

giant GPU, this is what it would look

like. This is the level of wafer scale

processing we would have to do.

It's incredible. All of this, all of

these chips are now put into one giant

rack.

Did I do that or did somebody else do

that? Into that one giant rack.

You know, sometimes I don't feel like

I'm up here by myself.

Just

this one giant rack makes all of these

chips work together as one. It's

actually completely incredible. And I'll

show you the benefits of that. The way

it looks is this. So, thanks, Janine.

I I like this. All right, ladies and

gentlemen, Janine Paul.

I got it. In the future next, I'm just

going to go like Thor.

It's like when you're at home and and

you can't reach the remote and you just

go like this and somebody brings it to

you. That's Yeah. Same idea.

It never happens to me. I'm just

dreaming about it. I'm just saying.

Okay. So, so anyhow, anyhow, um we

basically this is what we created in the

past. This is MVLink MVLink 8. Now,

these models are so gigantic. The way we

solve it is we turn this model, this

gigantic model, into a whole bunch of

experts. It's a little bit like a team.

And so, these experts are good at

certain types of problems. And we

collect a whole bunch of experts

together. And so, this giant

multi-rillion dollar AI model has all

these different experts and we put all

these different experts on a GPU. Now,

this is NVLink 72.

We could put all of the chips into one

giant fabric and every single expert can

talk to each other. So the master the

the primary expert could talk to all of

the true work and all of the necessary

contexts and prompts and bunch of data

that we have to bunch of tokens that we

have to send to all of the experts. The

experts would whichever one of the

experts are selected to solve the answer

would then go off and try to respond and

then it would go off and do that layer

after layer after layer. Sometimes

eight, sometimes 16 and sometimes these

experts, sometimes 64, sometimes 256.

But the point is there are more and more

and more experts. Well, here MVLink 72,

we have 72 GPUs. And because of that, we

could put four experts in one GPU. The

most important thing you need to do for

each GPU is to generate tokens, which is

the amount of bandwidth that you have in

HPM memory. We have one H one GPU

generating thinking for four experts

versus here because each one of the

computers can only put eight GPUs. We

have to put 32 experts into one GPU. So

this one GPU has to think for 32 experts

versus this system each GPU only has to

think for four. And because of that the

speed difference is incredible. And this

just came out. This is the benchmark

done by semi analysis. They do a really

really thorough job and they benchmarked

all of the GPUs that are benchmarkable

and it turns out it's not that many. If

you look at the list of looks list of

GPUs you could actually benchmark is

like 90% Nvidia. Okay. And but so we're

comparing ourselves to ourselves but the

second best GPU in the world is the H200

and runs all the workload.

Grace Blackwell per GPU is 10 times the

performance.

Now, how do you get 10 times the

performance when it's only twice the

number of transistors?

Well, the answer is extreme code design.

And by understanding the nature of the

future of AI models and we're thinking

across that entire stack, we can create

architectures for the future. This is a

big deal. It says we can now respond a

lot faster. But this is even bigger

deal. This next one, look at this. This

says

that the lowest cost tokens in the world

are generated by Grace Blackwell

MVLink72. The most expensive computer.

On the one hand, GB200 is the most

expensive computer. On the other hand,

its token generation capability is so

great that it produces it at the lowest

cost because the tokens per second

divided by the t by the total cost of

ownership of Grace Blackwell is so good.

It is the lowest cost way to generate

tokens. By doing so, delivering

incredible performance, 10 times the

performance, incre delivering 10 times

lower cost, that virtual cycle can

continue. Which then brings me to this

one. I just saw this literally

yesterday. This is uh the CSP capex.

People are asking me about capex these

days and um this is a good way to look

at it. In fact, the capex of the top six

CSPs and this one, this one is Amazon,

Core Weave, Google, Meta, Microsoft, and

Oracle. Okay, these CSPs together are

going to invest this much in capex. And

I would I would tell you the timing

couldn't be better. And the reason for

that is now we have the Grace Blackwell

MVLink72 in all volume production,

supply chain, everywhere in the world is

manufacturing it. So we can now deliver

to all of them this new architecture so

that the capex invests in instruments

computers that deliver the best TCO. Now

underneath this there are two things

that are going on. So when you look at

this it's actually fairly extraordinary

and it's fairly extraordinary anyhow.

But what's happening under underneath is

this there are two platform shifts

happening at the same time.

One platform shift is going from general

purpose computing to accelerated

computing. Remember accelerated

computing as I mentioned to you before

it does data processing, it does image

processing, computer graphics, it does

com comput computation of all kinds. It

runs SQL, runs spark, it runs, you know,

you you ask it, you tell us what you

need to have run, and I'm fairly certain

we have an amazing library for you. You

could be, you know, a data center trying

to make masks to manufacture

semiconductors. we have a great library

for you. And so underneath irrespective

of AI, the world is moving from general

purpose computing to accelerated

computing irrespective of AI. And in

fact, many of the CSPs already have

services that have been here long ago

before AI. Remember, they were invented

in the era of machine learning.

classical machine learning algorithms

like XG boost, algorithms like um uh

data frames that are used for recommener

systems, collaborative filtering,

content filtering, all of those

technologies were created in the old

days of general purpose computing. Even

those algorithms, even those

architectures are now better with

accelerated computing. And so even

without AI, the world's CSPs are going

to invest into acceleration. Nvidia's

GPU is the only GPU that can do all of

that plus AI. And ASIC might be able to

do AI, but it can't do any of the

others.

Nvidia could do all of that, which

explains why it is so safe to just lean

into Nvidia's architecture. We have now

reached our virtual cycle, our

inflection point. And this is quite

extraordinary. I have many partners in

the room and all of you are part of our

supply chain and I know how hard you

guys are working. I want to thank all of

you how hard you are working and thank

you very much.

Now I'm going to show you why

this is what's going on in our company's

business. We're seeing extraordinary

growth for Grace Blackwell for all the

reasons that I just mentioned. It's

driven by two exponentials. We now have

visibility.

I think we're probably the first

technology company in history to have

visibility into half a trillion dollars

of cumulative Blackwell and early ramps

of Reubin

through 2026. And as you know, 2025 is

not over yet and 2026 hasn't started.

This is how much business is on the

books. Half a trillion dollars worth so

far. Now, this is out of that. We've

already shipped 6 million of the

Blackwells in the first several

quarters. I guess the first four

quarters of production, three and a half

quarters of production. We still have

one more quarter to go for 2025. And

then we have four quarters. So the next

five quarters there's $500 million $500

billion half a trillion dollars. That's

five times the growth rate of Hopper.

That kind of tells you something. This

is Hopper's entire life. This doesn't

include China and and um and Asia. So

this is just uh the West. Okay. This is

just uh we're excluding China. So Hopper

in its entire life 4 million GPUs.

Blackwell. Each one of the black walls

has two GPUs in it in one large package.

20 million GPUs of Blackwells in the

early parts of Reuben. Incredible

growth. So, I want to thank all of our

supply chain partners. Everybody, I know

how hard you guys are working. I made a

video to celebrate your work. Let's play

it.

[Music]

The age of AI has begun.

Blackwell is its engine, an engineering

marvel.

In Arizona, it starts as a blank silicon

wafer.

Hundreds of chip processing and

ultraviolet lithography steps build up

each of the 200 billion transistors

layer by layer on a 12in wafer. In

Indiana, HBM stacks will be assembled in

parallel. HBM memory dies with 1,024

IO's are fabricated using advanced EUV

technology through silicon via is used

in the back end to connect 12 stacks of

HBM memory and base dye to produce HBM.

Meanwhile, the wafer is scribed into

individual Blackwell dye, tested and

sorted, separating the good dyes to move

forward. The chip on wafer on substrate

process attaches 32 Blackwell dyes and

128 HPM stacks on a custom silicon

interposer wafer.

[Music]

Metal interconnect traces are etched

directly into it, connecting Blackwell

GPUs and HBM stacks into each system and

package unit, locking everything into

place. Then the assembly is baked,

molded, and cured, creating the GB300

Blackwell Ultra Super Chip.

In Texas, robots will work around the

clock to pick and place over 10,000

components onto the Grace Blackwell PCB.

In California, Connect X8 Super Nix for

scaleout communications and Bluefield 3

DPUs for offloading and accelerating

networking, storage, and security are

carefully assembled into GB300 compute

trays.

MVLink is the breakthrough high-speed

link that Nvidia invented to connect

multiple GPUs and scale up into a

massive virtual GPU.

The NVLink switch tray is constructed

with MVLink switch chips providing 14.4

terabytes per second of all toall

bandwidth. MVLink spines form a custom

blindmated back plane with 5,000 copper

cables connecting all 72 black welds or

144 GPU dies into one giant GPU

delivering 130 terabytes per second of

all to all bandwidth nearly the global

internet's peak traffic.

Skilled technicians assemble each of

these parts into a rack scale AI

supercomput.

In total, 1.2 million components, 2 m of

copper cable, 130 trillion transistors,

weighing nearly 2 tons.

From silicon in Arizona and Indiana to

systems in Texas, Blackwell and future

Nvidia AI factory generations will be

built in America,

writing a new chapter in American

history and industry.

America's return to making and

reindustrialization,

reignited by the age of AI.

The age of AI has begun.

Made in America.

Made for the world.

[Music]

We are manufacturing in America again.

It is incredible. The first thing that

President Trump asked me for is bring

manufacturing back. Bring manufacturing

back because it's it's necessary for

national security. bring manufacturing

back because we want the jobs and we

want that part of the economy. And nine

months later, nine months later, we are

now manufacturing in full production

Blackwell in Arizona.

Extreme Blackwell GB 200 MV Grace

Blackwell Envy 72 Extreme code design

gives us 10x generationally. It's

utterly incredible. Now, the part that's

really incredible is this. This is the

first AI supercomputer we made. This is

in 2016 when I delivered it to a startup

in San Francisco which turned out to

have been OpenAI. This was the computer.

And in order to do the create that

computer, we designed one chip.

We designed one new chip in order for us

to do code design. Now, look at all of

the chips we have to do. This is what it

takes. You're not going to take one chip

and make a computer 10 times faster.

That's not going to happen. The way to

make computers 10 times faster that we

can keep increasing the performance

exponentially, we can keep driving cost

down exponentially is extreme code

design and working on all these

different chips at the same time. We now

have Reuben back home. This is Ruben.

This is the Vera Rubin and and Ruben.

Ladies and gentlemen, Ruben

This is this is our third generation

NVLink 72 rack scale computer. Third

generation GB200 was the first one. All

of our partners around the world, I know

how hard you guys worked. It was

insanely hard. It was insanely hard to

do. Second generation, so much smoother.

And this generation, look at this.

Completely cableless.

completely cableless. And this is this

is all back in the lab now. This is the

next generation Reuben. While we're

shipping GB300's,

uh we're preparing Reuben to be in

production, you know, this time next

year, maybe slightly earlier. And so

every single year, we are going to come

up with the most extreme code design

system so that we can keep driving up

performance and keep driving down the

token generation cost. Look at this.

This is just an incredibly beautiful

computer. Now,

so this is amazing. This is 100

pedaflops. I know this doesn't mean

anything. 100 pedlops, but

compared to the DGX1 I delivered to

OpenAI 10 years ago, nine years ago,

it's 100 times the performance right

here versus 100 times of that

supercomput. 100 times a 100 of those,

let's see, a hundred of those would be

like 25 of these racks all replaced by

this one thing.

One

Vera Rubin. Okay. So this is this is the

compute tray

and this is

so Vera Rubin super chip.

Okay. And this is the compute tray. This

Oh right here.

It's incredibly easy to install. Just

flip these things open, shove it in.

Even I could do it. Okay. And this is

the ver Vera Rubin compute tray. If you

decide you wanted to add a special

processor, we've added another

processor. It's called a context

processor because the amount of context

that we give AIS are larger and larger.

We wanted to read a whole bunch of PDFs

before it answer a question. Wanted to

read a whole bunch of archive papers,

watch a whole bunch of videos. Go learn

all this before you answer a question

for me. All of that context processing

could be added. And so you see on the

bottom eight connectx9

new super nicks. You have CX you have uh

CPXs eight of them. You have uh blue

field 4 this new data processor two Vera

CPUs and four Reuben packages or eight

Reuben GPUs. All of that in this one

node, completely cableless, 100% liquid

cooled. And then this new processor, I

won't talk too much about it today. I

don't have enough time, but this is

completely revolutionary. And the reason

for that is because your AIs need to

have more and more memory. You're

interacting with it more. You wanted to

remember our last conversation.

Everything that you've learned on my

behalf, please don't forget it when I

come back next time. And so all of that

memory is going to create this thing

called KV caching. And that KV caching

retrieving it, you might have noticed

every time you go into your your AIS

these days, it takes longer and longer

to refresh and retrieve all of the

previous conversations. And and the

reason for that is we need a

revolutionary new processor and that's

called Blue Fuel 4. Next is the the

ConnectX switch, excuse me, the MVLink

switch which is right here.

Okay, this is the MVLink switch. This is

what makes it possible for us to con

connect all of the computers together.

And this switch is now several times

the bandwidth of the entire world's peak

internet traffic. And so that spine is

going to communicate and carry all of

that data simultaneously to all of the

GPUs. On top of that, on top of that,

this is the this is the Spectrum X

switch. And this Ethernet switch was

designed so that all of the processors

could talk to each other at the same

time and not gum up the network. Gum up

the network. That's very technical.

Okay. So, um, so these are the these

three combined. And then this is the

quantum switch. This is for Infiniband.

This is Ethernet. We don't care what

language you would like to use, whatever

standard you like to use. We have great

scale out fabrics for you. Whether it's

Infiniban or Quant or Spectrum Ethernet,

this one uses silicon photonics and is

completely co-acked options. Basically,

the laser comes right up to the silicon

and connects it to our chips. Okay. So,

this is the Spectrum X Ethernet. And so,

now let's talk about Thank you. Oh, this

is this is what it looks like. This is a

rack.

This is two and a half. This is two uh

2000. This is two tons.

1.5 million parts.

And the spine, this spine right here

carries the entire internet traffic in

one second. Same speed moves across all

of these different processors. 100%

liquid cooled. All for the, you know,

fastest token generation rate in the

world. Okay, so that's what a rack looks

like. Now that's one rack. A gigawatt

data center would have

you know call it

let's see 16 racks would be a th00and um

and then 500 of those. So whatever 500

time 16 and so call it 9,000 of these

8,000 of these would be a one gigawatt

data center. Okay. And so that's a

future AI factory. Now we used, as you

notice, Nvidia started out by designing

chips and then we started to design

systems and we designed AI

supercomputers. Now we're designing

entire AI factories. Every single time

we move out and we integrate more of the

problem to solve, we come up with better

solutions. We now build entire AI

factories.

This is going this AI factory is what

we're building for Vera Rubin and we

created a technology that makes it

possible for all of our partners to

integrate into this factory digitally.

Let me show it to you.

The next industrial revolution is here

and with it a new kind of factory.

AI infrastructure is an ecosystem scale

challenge

requiring hundreds of companies to

collaborate.

NVIDIA Omniverse DSX is a blueprint for

building and operating gigascale AI

factories.

For the first time, the building, power,

and cooling are co-designed with

NVIDIA's AI infrastructure stack.

It starts in the Omniverse digital twin.

Jacob's engineering optimizes compute

density and layout to maximize token

generation according to power

constraints.

They aggregate SIM ready open USD assets

from Seammen's Schneider Electric Train

and Vertive into PTC's product life

cycle management.

Then simulate thermals and electricals

with CUDA accelerated tools from EAB

and Cadence.

[Music]

Once designed, Nvidia partners like

Bectal and Vertive deliver

pre-fabricated modules factory-built,

tested, and ready to plug in. This

shrinks build time significantly,

achieving faster time to revenues.

When the physical AI factory comes

online,

the digital twin acts as an operating

system.

Engineers prompt AI agents from FIDRA

and Emerald AI, previously trained in

the digital twin to optimize power

consumption and reduce strain on both

the AI factory and the grid.

In total, for a one gigawatt AI factory,

DSX optimizations can deliver billions

of dollars in additional revenue per

year.

Across Texas,

Georgia, and Nevada, Nvidia's partners

are bringing DSX to life.

In Virginia, NVIDIA is building an AI

factory research center using DSX to

test and productize Vera Rubin from

infrastructure to software.

With DSX, Nvidia partners around the

world can build and bring up AI

infrastructure faster than ever.

[Music]

completely completely in digital long

long before Vera Rubin exists as a real

computer we've been using it as a

digital twin computer now long before

these AI factories exist we will use it

we will design it we'll plan it we'll

optimize it and we'll operate it as a

digital twin and so all of our partners

that are working with us I'm incredibly

happy for all of you supporting us and

Gio is here and uh G ver Vernova is

here. Schlider, I I think um I think uh

uh Olivier is here. Olivia Blum is here.

Um uh uh Seaman's incredible partners.

Okay, Roland Bush, I think he's

watching. Hi, Roland. And so anyways, uh

really really great partners working

with us.

In the beginning, we had CUDA and we

have all these different ecosystems of

software partners. Now we have Omniverse

DSX and we're building AI factories and

again we have these incredible ecosystem

of partners working with us. Let's talk

about models.

Open source models particularly in the

last couple years several things have

happened. One, open source models have

become quite capable because of

reasoning capabilities. It has become

quite capable because they're

multimodality and they're incredibly

efficient because of distillation. So

all these different capabilities have

become uh has made open source models

for the very first time incredibly

useful for developers. They are now the

lifeblood of startups.

Lifeblood of startups in different

industries because obviously as I

mentioned before each one of the

industries have its own use case, its

own use cases, it own data, it owns

data, its own flywheels. All of that

capability, that domain expertise needs

to have the ability to embed into a

model. Open source makes that possible.

Researchers need open source. Developers

need open-source. Companies around the

world, we need open source. Open- source

models is really, really important.

The United States has to lead in open

source as well. We have amazing

proprietary models. We have amazing

proprietary models. We need also amazing

open source models. Our country depends

on it. Our startups depend on it. And so

Nvidia is dedicating ourselves to go do

that. We are now the largest the largest

we lead in open-source contribution. We

have 23 models in leaderboards. We have

all these different domains from

language models the physical AI models.

I'm going to talk about robotics models

to biolog biology models. Each one of

these models has enor enormous teams and

that's one of the reasons why we built

supercomputers for ourselves to enable

all these models to be created. We have

number one speech model, number one

reasoning model, number one physical AI

model. The number of downloads is really

really terrific. We are dedicated to

this and the reason for that is because

science needs it, researchers need it,

startups need it and companies need it.

I'm delighted that AI startups build on

Nvidia. They do so for several reasons.

One, of course, our ecosystem is rich.

Our tools work great. All of our tools

work on all of our GPUs. Our GPUs are

everywhere. It's literally in every

single cloud. It's available on prem.

You could build it yourself. You could

you could, you know, build up a an

enthusiast gaming PC with multiple GPUs

in it and you could download our

software stack and it it just works. We

have the benefit of rich developers who

are making this ecosystem richer and

richer and richer. So, I'm really

pleased with all of the startups that

we're working with. I'm I'm thankful for

that. It is also the case that many of

these startups are now starting to

create even more ways to enjoy our GPUs.

the Cordweaves, Nscale, Nbius, Llama,

Lambda, all of these companies, Crusoe

companies are building these new GPU

clouds to serve the startups and I

really appreciate that this is all

possible because NVIDIA is everywhere.

We integrate our libraries, all of the

CUDA X libraries I talked to you about,

all the open-source AI models that I

talked about, all of the models that I

talked about, we integrated into AWS,

for example. really love working with

Matt. We integrated into Google Cloud,

for example. Really love working with

Thomas. Each one of these clouds

integrate NVIDIA GPUs and our computing,

our libraries as well as our models.

Love working with Satia over at

Microsoft Azure. Love working with uh

Clay at Oracle. Each one of these clouds

integrate the NVIDIA stack. As a result,

wherever you go, whichever cloud you

use, it works incredibly. We also

integrate Nvidia libraries into the

world SAS so that each one of these SAS

will eventually become agentic SAS. I

love Bill McDerman's vision for Service

Now. They're the Yeah, there you go. I

think that might have been Bill.

Hi, Bill. And so Service Now, what is

it? 85% of the world's enterprise

workloads workflows SAP 80% of the

world's commerce Christian Klein and I

are working together to integrate NVIDIA

libraries CUDA X and Nemo and Neotron

all of our AI systems into SAP working

with Cine over at Synopsis accelerating

the world CAE CAD EDA tools so that they

could be faster and could scale helping

them create AI agents one of these days

I would love to hire a AI I agent ASIC

designers to work with our ASICH

designers essentially the cursor of

Synopsis if you will. We're working with

uh Annie Rude. Annie Rude is here. I saw

him earlier today. He was part of the

pregame show. Cadence doing incredible

work accelerating their stack creating

AI agents so that we can have cadence AI

as designers and system designers

working with us. Today we're announcing

a new one.

AI

will supercharge productivity. AI will

transform just about every industry, but

AI will also supercharge

cyber security challenges, the bad AIs.

And so we need an incredible defender. I

can't imagine a better defender than

Crowd Strike. George George is here. Uh

he was Yep, I saw him earlier. We are

partnering with Crowdstrike to make

cyber security speed of light to create

a system that has cyber security AI

agents in the cloud but also incredibly

good AI agents on prim or at the edge.

This way you whenever there's a threat

you are moments away from detecting it.

We need speed and we need a fast agentic

AI super a super smart AIS.

I have a second announcement. This is

the single fastest enterprise enterprise

company in the world.

Probably the single most important

enterprise stack in the world today.

Palunteer ontology.

Anybody from Palunteer here? I was just

talking to Alex earlier.

This is Palunteer ontology. They take

information,

they take data, they take human judgment

and they turn it into business insight.

We work with Palanteer to accelerate

everything Palunteer does so that we

could do data processing data processing

at a much much larger scale and more

speed whether it's structured data of

the past and of course we'll have

structured data, human recorded data,

unstructured data and process that data

for our government, for national

security and for enterprises around the

world. process that data at speed of

light and to find insight from it. This

is what it's going to look like in the

future. Palunteer is going to integrate

Nvidia so that we could process at the

speed of light and at extraordinary

scale.

Okay, Nvidia and Palunteer.

Let's talk about physical AI. Physical

AI requires three computers just as it

takes two computers to train a language

model. One that's to train it, evaluate

it, and then inference it. Okay, so

that's the large GB200 that you see. In

order to do it for physical AI, you need

three computers. You need the computer

to train it. This is GB the Grace

Blackwell NVLink 72. We need a computer

that does all of the simulations that I

showed you earlier with Omniverse DSX.

It basically is a digital twin for the

robot to learn how to be a good robot

and for the factory to essentially be a

digital twin. That computer is the

second computer, the omniverse computer.

This computer has to be incredibly good

at generative AI and it has to be good

at computer graphics, sensor simulation,

ray tracing, signal processing. This

computer is called the omniverse

computer. And once we train the model,

simulate that AI inside a digital twin

and that digital twin could be a digital

twin of a factory as long as well as a

whole bunch of digital twins of robots.

Then you need to operate that robot. And

this is the robotic computer. This is

this one goes into a self-driving car.

Half of it could go into a robot. Okay?

Or you could actually have, you know,

robots that are quite agile and quite

quite fast in operations. And it might

take two of these computers. And so this

is the Thor Jetson Thor robotics

computer. These three computers all run

CUDA. And it makes it possible for us to

advance physical AI. AI that understand

the physical world, understand laws of

physic causality

permanence, you know,

physical AI. We have incredible partners

working with us to create the physical

AI of factories. We're using it

ourselves to create our factory in

Texas. Now, once we create the robotic

factory, we have a bunch of robots that

are inside it. And these robots also

need the physical AI applies physical AI

and works inside the digital twin. Let's

take a look at it.

America is re-industrializing,

reshoring manufacturing across every

industry. In Houston, Texas, Foxcon is

building a state-of-the-art robotic

facility for manufacturing NVIDIA AI

infrastructure systems.

With labor shortages and skills gaps,

digitalization, robotics, and physical

AI are more important than ever.

The factory is born digital

in Omniverse.

Foxcon engineers assemble their virtual

factory in a seaman's digital twin

solution developed on Omniverse

Technologies. Every system, mechanical,

electrical, plumbing, is validated

before construction.

Seaman's plant simulation runs design

space exploration optimizations

to identify ideal layout.

When a bottleneck appears, engineers

update the layout with changes managed

by Seaman's team center.

In Isaac sim, the same digital twin is

used to train and simulate robot AIS.

In the assembly area, FANIC manipulators

build GB300 tray modules

by manual manipulators from FII and

Skilled AI install bus bars into the

trays

and AMRs shuttle the trays to the test

pods.

Then Foxcon uses Omniverse for

large-scale sensor simulation where

robot AIs learn to work as a fleet.

In Omniverse, vision AI agents built on

NVIDIA Metropolis and Cosmos.

Watch the fleets of robots and workers

from above to monitor operations

and alert Foxcon engineers of anomalies

and safety violations.

or even quality issues.

And to train new employees, agents power

interactive AI coaches for easy worker

onboarding.

The age of US re-industrialization is

here with people and robots working

together.

[Music]

That's the the future of manufacturing,

the future of factories. I want to thank

our partner Foxcon Younglu, the CEO, is

here, but all of these ecosystem

partners make it possible for us to

create the future of robotic factories.

The factory is essentially a robot

that's orchestrating robots

to build things that are robotic. You

know this is the amount of software

necessary to do this is so intense that

unless you could do it inside a digital

twin to to plan it to design it to

operate inside a digital twin the hopes

of getting this to work is nearly

impossible. I'm so happy to see also

that Caterpillar, my friend Joe Joe

Creed and his hundred-year-old company

is also incorporating digital twins and

the way they manufacture. Um, these

factories will have future robotic

systems and one of the most advanced is

figure. Brett Adcock is here today. He

just he founded a company three and a

half years ago. They're worth almost $40

billion. Today we're working together in

training the the AI, training the robot,

simulating the robot and of course the

robotic computer that goes into figure

really amazing. Uh I had the benefit of

seeing it. Uh it's really quite quite

extraordinary. It is very likely that

humano robots and uh my friend Elon is

also working on this that this is likely

going to be one of the largest consumer

new consumer electronics markets and

surely one of the largest industrial

equipment market. Peggy Johnson and the

folks at Agility are working with us on

robots for warehouse automation. the

folks at Johnson Johnson working with us

again training the robot simulating it

in digital twins and also operating the

robot these John Johnson and Johnson

surgical robots are even going to

perform surgery that are completely

noninv non-invasive surgery at a

precision the world's never seen before

and of course the cutest robot ever the

cutest robot ever the Disney robot and

this is this is um something really

close to our heart. We're working with

Disney research on a entirely new

framework and sim simulation platform uh

based on revolutionary technology called

Newton. And that Newton uh simulator

makes it possible for the the robot to

learn how to be a good robot inside a

physically aware, physically based

environment. Let's take a look at it.

[Music]

See

[Music]

[Music]

that?

[Music]

Blue, ladies and gentlemen. Disney Blue.

Tell me you that's not adorable. He's

not adorable.

We all want one. We all want one. Now

remember everything you were just seeing

that is not animation. It's not a movie.

It's a simulation. That simulation is an

omniverse. Omniverse the digital twin.

So these digital twins of factories,

digital twins of warehouses, digital

twins of surgical rooms, digital twins

where blue could learn how to manipulate

and navigate and you know interact with

the world. All completely done in real

time. This is going to be the largest

consumer electronics product line in the

world. Some of them are just really

working incredibly well now. This is the

future of human or robotics and of

course blue. Okay.

Now, human robots is still in

development. But meanwhile,

there's one robot that is clearly at an

inflection point and it is basically

here. And that is a robot on wheels.

This is a robo taxi. A robo taxi is

essentially an AI chauffeur. Now, one of

the things that we're doing today, we're

announcing the NVIDIA drive Hyperion.

This is a big deal.

We created this architecture so that

every car company in the world could

create cars, vehicles could be

commercial, could be passenger, could be

dedicated to robo taxi. Create vehicles

that are robo taxi ready. The sensor

suite with surround cameras and radars

and LAR

make it possible for us to achieve the

highest level of surround cocoon sensor

perception and redundancy necessary for

the highest level of safety.

Hyperion drive drive Hyperion is now

designed into Lucid Mercedes-Benz my

friend Ola Ken can kenas um the folks at

Stalantis and there are many other cars

coming and once you have a basic

standard platform then developers of AV

systems and there's so many talented

ones wave wabby Aurora momenta neuro

there's so many of them we ride there's

so many of them that can then take their

AV system and run it on the standard

chassis. Basically, the standard chassis

has now become a computing platform on

wheels. And because it's standard and

the sensor suite is comprehensive, all

of them could deploy their AI to it.

Let's take a quick look.

[Music]

Okay, that's the be that's beautiful San

Francisco. And as you could see, as you

could see, robo taxis inflection point

is about to get here. And in the future,

a trillion miles a year that are driven,

a 100 million cars made each year.

There's some 50 million taxis around the

world. It's going to be augmented by a

whole bunch of robo taxis. So, it's

going to be a very large market to

connect it and deploy it around the

world. Today, we're announcing a

partnership with Uber. Uber Dar Dara K

Dara is going to we're working together

to connect these Nvidia drive Hyperion

cars into a global network and now in

the future you'll you know be able to

hail up one of these cars and the

ecosystem is going to be incredibly rich

and we'll have Hyperion or Robo taxi

cars all over the world. This is going

to be a new computing platform for us

and I'm expecting it to be quite

successful.

Okay.

So this is what we talked about today.

We talked about a large large number of

things. We spoke about remember at the

core of this is two or two platform

transitions from general purpose

computing to accelerated computing.

Nvidia CUDA and those suite of libraries

called CUDA X has enabled us to address

practically every industry and we're at

the inflection point. It is now growing

as a virtual cycle would suggest. The

second inflection point is now upon us.

The second platform transition AI from

classical handwritten software to

artificial intelligence. Two platform

transitioning happening at the same time

which is the reason why we're feeling

such incredible growth.

Quant quantum computing. We spoke about

open models. spoke about we spoke about

enterprise with crowd strike and uh

Palunteer accelerating their platforms.

Uh we spoke about robotics a new large

potentially one of the largest consumer

electronics and industrial manufacturing

sectors and of course we spoke about 6G.

Nvidia has new platforms for 6G we call

it ARC. We have a new platform for

robotics cars we call that Hyperion.

We have new platforms

even for factories. Two types of

factories. The AI factory we call that

DSX and then factories with AI we call

that mega. And so now we're also

manufacturing in America. Ladies and

gentlemen, thank you for joining us

today and thank you for allowing me to

bring

Thank Thank you for for allowing us to

bring GTC to Washington DC. We're going

to do it hopefully every year. And thank

you all for your service and making

America great again. Thank you.

[Music]

We start with a handshake. Solid and

true. One step at a time, we're breaking

through. Brick by brick, we're stacking

dreams high. Side by side, we'll touch

the sky. Handshakes and high hopes we're

making our way. Shoulder to shoulder.

Come what may

[Music]

rolling as one.

[Music]

Plans on paper but hearts in sink.

Building together faster than you think.

Laughter's the glue in the grind we

share. We've got the spark. We're going

somewhere. Handshakes and high hopes.

We're making our way shoulder to

shoulder. Come what may shed vision

brighter than the sun.

Friendship and business

and shakes and high making our way

shoulder to come one way. Vision

brighter than the sun.

Business rolling as one.

[Music]