黃仁勳GTC DC演講！NVIDIA 宣布美國製 AI 超級工廠計畫｜Jensen Huang: NVIDIA Announces U.S.-Made AI Superfactory

welcome to the stage Nvidia founder and CEO Jensen Wang

Washington DC

Washington DC welcome to GTC

it's hard not to be sentimental and proud of America

I gotta 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

so they're here to meet with you and uh

a really great we couldn't do what we do

without all of our ecosystem partners

this is the Super Bowl of AI

people say and therefore

every Super Bowl should have an amazing pre game show

what do you guys think about the pre game show

and our all all star

all star athletes and all star 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 general

purpose 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

be limited by the laws of physics

and that moment has now arrived

Denard scaling has stopped

it's called Denard scaling

Denard 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 hands

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 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

cuLitho computational lithography

it took us nearly seven years to get here with cuLitho

and now TSMC uses it Samsung uses it

ASML uses it this is an incredible library

for computational lithography

the first step of making a chip

sparse solvers for CAE applications

coop a numerical optimization

has broken just about every single record

the traveling salesman problem

how to connect millions of products

with millions of customers in the supply chain

Warp Python Solver for CUDA for Simulation

cuDF a data frame approach

basically accelerating sequel Dataframe

Dataframe databases

this library is the one that started AI all together

cuDNN

the 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

by the way

we're not gonna talk a lot about healthcare today

but be sure to see Kimberly's keynote

she's gonna talk a lot about the work that we do

in healthcare and the list goes on

genomics processing

Aerial, pay attention

we're gonna do something really important here today

um cuQuantum

quantum computing this is just a representative

of 350 different libraries in our company

and each one of these libraries

redesign 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

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 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 gonna 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 defined 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 this 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 in a

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 gonna do that

we have a big partnership with Nokia

Nokia is the second largest

telecommunications maker in the world

it's a three trillion dollar 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 Ark

The Aerial Radio Network Computer

Aerial ran computer

arc Ark is built from three fundamental

new technologies the gray CPU

the Blackwell GPU

and our Connect X Melonox 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 gonna create for the first time

a software defined programmable computer

that's able to communicate wirelessly

and do AI processing at the same time

this is completely revolutionary

we call it Nvidia Ark and Nokia is gonna 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 gonna partner with Nokia

they're gonna make Nvidia Ark their future base station

Nvidia Ark is also compatible with Aerial scale

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 beamforming 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 one 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 ran

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 spectrum efficiency

the second is AI on ran 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 Feynman

imagine a new type of computer

that can simulate nature directly

to simulate nature directly

because nature is quantum

he called it a quantum computer

forty years later

the industry has made a fundamental breakthrough

forty years later just last year

a fundamental breakthrough

it is now possible to make one logical qubit

one logical qubit one logical qubit that's coherent

stable and error corrected in the past

now that one logical qubit consists of

could be sometimes tens

sometimes hundreds of physical qubits

all working together as you know qubits

these particles are incredibly fragile

they could be unstable very easily

any observation any sampling of it

any environmental condition

causes it to become de coherent

and so

it takes extraordinarily well controlled environments

and now also

a lot of different physical qubits for them to work

together

and for us to do error correction on these

what it called auxiliary or syndrome qubits

for us to error correct them

and infer what that logical qubit 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 the 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 qubits quantum bits

as its core building block

but no matter the method all qubits

whether superconducting qubits

trapped ions neutral atoms

or photons share the same challenge

they're fragile and extremely sensitive to noise

today's qubits 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 qubit

which destroys the information inside it

the trick is to add extra qubits entangled

so that measuring them gives us enough information

to calculate where errors occurred

without damaging the qubits

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 qubits

calculating where errors occur

and sending data back to correct them

NVQ 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 CUDA-Q

our open platform for quantum GPU computing

using NVQ Link and CUDA-Q

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 NVQ Link

NVQ 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

to error correction for today's number of few qubits

it does error correction for tomorrow

where we're gonna essentially

scale up these quantum computers

from the hundreds of qubits we have today

to tens of thousands of qubits

hundreds of thousands of qubits in the future

so we now have an architecture that can do control

cosimulation 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 CPU

and have the two processors

CPU and the GPU work

and have computation move back and forth

within just a few microseconds

the essential latency

to be able to cooperate with the quantum computer

so now CUDA-Q 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 Brookhaven

Fermi Labs in Chicago Lincoln Laboratory

Los Alamos Oak Ridge

Pacific Northwest Sandia 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

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 accelerate computing

that's why every future supercomputer will be GPU based

supercomputer

we're going to AI so that AI and principal solvers

principal simulation

principal physics simulation is not gonna go away

but it could be augmented and hands scaled

you surrogate models AI models working together

we also know that principal solvers

classical computing

can 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 that 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

ChatGPT

is at the forefront of what people would consider AI

however just as you see right now

these scientific supercomputers

are not gonna run chatbots

they're gonna do basic science

science AI the world of AI is much

much more than a chatbot of course

the chatbot is extremely important

and AI is fundamentally critical

deep computer science incredible computing

great breakthroughs are still essential for AI

but beyond that AI is a lot more

AI is in fact

I'm gonna describe AI in a couple different ways

that's 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 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 consists consists of giant data centers

like easily many times the size of this

size of this room a 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 can tokenize chemicals and proteins and genes

you can 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 ChatGPT

and it could generate just as ChatGPT can generate

so

all of the fundamental things that you see ChatGPT 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

in the last several years

we enjoyed the invention

and went through the innovation breakthroughs

of multimodality

there's so so many different types of models

there's CNN models

convolutional neural network models

there's state space models

there's graph neural network models

multimodal models of course

all the different tokenizations

and token methods that I just described

you could have models that are spatial

and it's 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 are 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 Aravind's doing at Perplexity

Perplexity

using web browsers to book vacations or do shopping

basically an AI using tools

ursor is an AI

an agentic AI system that we use at Nvidia

every single software engineer at Nvidia uses Cursor

has 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

these different industries

whether it's chatbots 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 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

it 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 pre compute 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 sure

it's unlike a data center of the past

it's 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 general purpose 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 gonna be big it's gonna be the future

and somehow a few months ago

it kicked into Turbo Charge

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 ourselves 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 technology

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's gonna do you know

they are really good at this

so they're gonna 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

guys if anything happens

I wanna be the first one out

just kidding 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 Learned 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 a 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

Cursor is helping

a several hundred thousand dollar 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

Synthesia Abridge

OpenEvidence, the list goes on

of course OpenAI

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 people 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 a virtuous cycle

essential for almost any industry

essential for any platform industry

it was essential for Nvidia

we have now reached a 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 developers wanna 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 compute 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 2 so that we keep this virtuous 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 virtuous cycle is now spinning

but how do we do that

when Moore's Law has really reached this limit

well the answer is called CO-DESIGN

you can't just design chips

and hope that things at

on top of it is gonna 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'll just keep adding more transistors

however that's all in percentages

not exponentials

we need to compound exponentials

to keep this virtuous cycle going

we call it extreme CO-DESIGN

and 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 CO-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

CO-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 you have to meet me like halfway

alright so this is kind of like Captain America's shield

so

NVLink 72 NVLink 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

yeah alright

ladies and gentlemen Janine Paul

I got it in the future

next I'm just gonna 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 MVLinks, 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 trillion 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 student work

and all of the necessary context 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 two 56

but the point is they're more and more and more experts

well here

NVLink 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 HBM memory

we have one H1 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 can 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 CO-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

and NVLink 72 the most expensive computer

on the one hand

GB two hundred 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

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 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 Google OK

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 MVLink 72

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

and 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 comp computation

of all kinds

it runs sequel 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 CSP's 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 XGBoost

algorithms like um

data frames that are used for recommender 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 CSP's are going to invest into acceleration

and

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 wanna thank all of you how hard you are working

thank you very much

now I'm gonna 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 Rubin 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 Blackwells has two GPUs in it

in one large package 20 million GPUs of Blackwells

in the early parts of Rubin

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

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 12 inch wafer

in Indiana HBM stacks will be assembled in parallel

HBM memory dies with 1,024 iOS

are fabricated using advanced EUV technology

Through-Silicon Vias is used in the back end

to connect 12 stacks of HBM memory

and base die to produce HBM

meanwhile

the wafer is scribed into individual Blackwell die

tested and sorted

separating the good dies to move forward

the chip on wafer on substrate process attaches

32 Blackwell dies and 128 HBM stacks

on a custom silicon interposer wafer

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 GB three hundred Blackwell Ultra Superchip

in Texas robots will work around the clock

to pick and place over 10,000 components

onto the Grace Kelly PCB

in California

Connect X8 Super NICs for scale out communications

and Bluefield 3 DPUs

for offloading and accelerating networking

storage and security are carefully assembled

into GB three hundred compute trays

NVLink 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 NVLink switch chips

providing 14.4 terabytes per second

of all to all bandwidth

NVLink spines form a custom blind mated backplane

with 5,000 copper cables connecting all 72 Blackwells

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

skill technicians assemble each of these parts

into a rack scale AI supercomputer

in total 1.2 million components

two miles of copper cable

130 trillion transistors weighing nearly two 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 re industrialization

reignited by the age of AI

the age of AI has begun

made in America

made for the world

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

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 NVLink 72 Extreme

CO-DESIGN gives us 10 x 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 that

create that computer we designed one chip

we designed one new chip

in order for us to do CO-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 gonna 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 CO-DESIGN

and working on all these different chips

at the same time we now have Rubin back home

this is Rubin this is the Vera

Rubin and

and the Rubin

ladies and gentlemen Rubin

this is this is our third generation

NVLink 72 rack scale computer

third generation GB two hundred 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 Rubin

while we're stripping GB three hundreds

we're preparing Rubin to be in production

you know this time next year

maybe slightly earlier and so every single year

we are gonna come up with the most extreme

CO-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 this

so this is amazing this is 100 petaflops

I know this doesn't mean anything

100 petaflops

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 supercomputer

a hundred times a hundred of those

let's see

a hundred of those would be like 25 of these racks

all replaced by this one thing

1

Vera Rubin okay

so this is this is the compute tray

and this is

so very Rubin super chip

okay and this is the computer tray

this up right here

it's incredibly easy to install

just flip these things open

shove it in even I could do it

OK and this is the Vera

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

AI's are larger and larger

we wanted to read a whole bunch of PDFs

before an 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

Connect ConnectX-9

new SuperNIC you have CX

you have a CPX's eight of them

you have a Bluefield 4 this new data processor

two Vera CPUs and four Rubin packages

or eight Rubin 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 AI's 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 op

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 Bluefield 4

next is the the Connect X switch

excuse me the NVLink switch

which is right here

okay this is the NVLink switch

this is what makes it possible for us

to 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 Infiniband or quantum or Spectrum Ethernet

this one uses silicon photonics

and is completely co packaged 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

2 thousand 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 it across all of these different processors

hundred percent 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 1,000 um

and then 500 of those so whatever

500 times 16 and so call it

9,000 of these

8,000 of these would be a 1 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

Jacobs Engineering

optimizes compute density and layout

to maximize token generation

according to power constraints

they aggregate SIM ready open USD assets from Siemens

Schneider Electric Train

and Vertiv into PTC's product lifecycle management

then simulate thermals and electrics with CUDA

accelerated tools from ETAP and Cadence

once designed and video

partners like Bechtel and Vertiv

deliver prefabricated 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 Phaidra 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 1 gigawatt AI factory

DSX optimizations

can deliver billions of dollars in additional revenue

per year

across Texas

Georgia and Nevada

envious 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

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

will 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 GE is here and GE Vernova is here

Schlitter I think

I think uh uh

Olivia's here Olivia Bloom is here

um uh

uh Siemens incredible partners OK

Roland Bush I think he's watching

hi Roland

and so anyways 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 their multimodality

and they're incredibly efficient

because of distillation

so all of 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 case its own data

its own use 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 to physical AI models

I'm gonna talk about robotics models to biology models

each one of these models has enormous teams

and that's one of the reasons

why we build 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 just works

and 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 CoreWeave, Nscale

Nebius Llama

LA Lambda all of these companies

Crusoe uh

companies

are building these new GPU clouds to serve the stars

and I really appreciate that this is all possible

because Nvidia is everywhere

we integrate our libraries

all of the CUDA X libraries I talk 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 Satya over a Microsoft Azure

love working with

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 SaaS

so that each one of these SaaS

will eventually become a gentic SaaS

I love Bill McDermott's vision for service now

there don't 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

work flows SAP

80% of the world's commerce

Christian Klein and I are working together

integrate Nvidia libraries

CUDA-X, NeMo, and Nemotron

all of our AI systems into SAP

working with Cecile over at synopsis

accelerating the World of CAE

Cadence EDA tools

so that they could be faster and get scale

helping them create AI agents

one of these days I would love to hire a AI agent

ASIC designers to work with our ASIC designers

essentially the Cursor of synopsis

if you will we're working with uh

Annie Rude Annie Rude 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

ASIC 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 AI's

and so we need an incredible defender

I can't imagine a better defender than crowd strike

George George is here

uh he was here

yep I saw him earlier

we are partnering with CrowdStrike

to make cybersecurity speed of light

to create a system that has cybersecurity

AI agents in the cloud

but also incredibly good AI agents

on prem 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 agent super smart AIs

I have a second announcement

this is the single fastest

enterprise company in the world

probably the single most important enterprise stack

in the world today

Palantir Ontology

anybody from Palantir here

I just I was just talking to Alex earlier

this is Palantir Ontology

they take information

they take data they take human judgement

and they turn it into business insight

we work with Palantir

to accelerate everything Palantir 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 gonna look like in the future

Palantir is going to integrate Nvidia

so that we could process at the speed of light

in an extraordinary scale

okay Nvidia and Palantir

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 G B200 that you see

in order to do it for physical AI

you need three computers

you need a 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, DDS

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 OK

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 physics 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 reindustrializing

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

Foxconn engineers assemble their virtual factory

in a Siemens digital twin solution

developed on Omniverse Technologies

every system mechanical

electrical plumbing

is validated before construction

Siemens Plant Simulation runs design

space exploration optimizations

to identify ideal layout

when a bottleneck appears

engineers update the layout with changes

managed by Siemens Teamcenter

in Isaac Sim the same digital twin is used

to train and simulate robot AI's

in the assembly area

FANUC manipulators build GB three hundred tray modules

by manual manipulators from F I I

and skilled AI install bus bars into the trays

and AMRs shuttle the trays to the test pods

then

Foxconn uses omnivrs 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 Foxconn

engineers of anomalies

and safety violations

or even quality issues

and to train new employees

agents power

interactive AI coaches for easy worker on boarding

the age of US reindustrialization is here

with people and robots working together

that's the the future of manufacturing

the future of factories I want to thank our partner

Foxconn Young Liu

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 Creed and his hundred year old company

is also incorporating digital twins

in the way they manufacture

um these factories will have future robotic systems

and one of the most advanced is figure

Bread at Cock 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 and training the

the AI training the robot

simulating the robot and of course

the robotic computer that goes into figure

really amazing I had the benefit of seeing it

it's really quite quite extraordinary

it is very likely that humanoid robots

and 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 Johnson and Johnson surgical robots

are even going to perform surgery

that are completely 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 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

blue ladies and gentlemen

Disney blue tell me 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 humanoid 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 lidar

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 Källenius

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

Wayve Waabi

Aurora, Momenta, and Nuro

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

okay that's the

that's beautiful San Francisco

and as you could see as you could see

robotaxis inflection point is about to get here

and in the future

a trillion miles a year that are driven

100 million cars made each year

there's some 50 million taxis around the world

it's gonna be augmented by a whole bunch of robotaxis

so it's gonna be a very large market

to connect it and deploy it around the world

today we're announcing a partnership with Uber

Uber Dara

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 robotaxi 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

are 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

2 platform transitioning happening at the same time

which is the reason

why we're feeling such incredible growth

quantum quantum computing

we spoke about open models

we spoke about we spoke about enterprise

with CrowdStrike and Palantir

accelerating their platforms

uh we spoke about robotics

a new 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 Ark

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 gonna do it hopefully every year

and thank you all for your service

and making America great again thank you