Original subtitles

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Separation confirmed. well beyond it.

Together we take the next great leap

into a bright new future

built for all mankind.

And here

is where it all begins.

Welcome to the stage, Nvidia founder and

CEO, Jensen Wong.

Welcome to GTC.

I just want to remind you this is a tech

conference.

All these people lining up so early in

the morning. All of you in here, it's

great to see you.

GTC

GTC. We're going to talk about

technology. We're going to talk about

platforms. Nvidia has three platforms.

You think that we mostly talk about one

of them. It's related to CUDA X. Our

systems is another platform and now we

have a new platform called AI factories.

We're going to talk about all of them

and most importantly we're going to talk

about ecosystems. But before I start,

let me thank our pregame show hosts. I

thought they did a great job. Sarah Go

of Conviction,

Alfred Lyn, Sequa Capital, Nvidia's

first venture capitalist, Gavin Baker,

Nvidia's first major institutional

investor. These three people are deep in

technology, deep in what's going on and

of course they have just a really broad

reach of technology ecosystem. And then

of course all of the VIPs that I hand

selected to join us today, allstar team.

I want to thank all of you for that.

I also want to thank all the companies

that are here.

Nvidia as you know is a platform

company. We have technology, we have our

platforms, we have re rich ecosystem and

today there are probably 100% of the

hundred trillion dollars of industry

here. 450 companies sponsored this

event. I want to thank you. A thousand

technical sessions, 2,000 speakers. This

is this conference is going to cover

every single layer of the five layer

cake of artificial intelligence from

land power and shell the infrastructure

to chips to the platforms the models and

of course the most important and

ultimately what's going to take get this

industry taken off is all of the

applications.

What it all began it all began here.

This is the 20th anniversary of CUDA.

We've been working on CUDA for 20 years.

For 20 years, we've been dedicated to

this architecture. This revolutionary

invention, SIMT, single instruction,

multi-threaded, writing scalar code

could spawn off into multi-threaded

application. much much easier to program

than CINDI. We recently added tiles so

that we could help people program tensor

cores and the structures of mathematics

that are so foundational to artificial

intelligence today.

Thousands of tools and compilers and

frameworks and libraries

in open source. There's a couple of

hundred thousand public projects. CUDA

literally is integrated into every

single ecosystem.

This chart

basically describes 100% of Nvidia's

strategies. You've been watching me talk

about this slide from the very

beginning. And ultimately, the single

hardest thing to achieve is the thing on

the bottom, installed base. It has taken

us 20 years to now have built up

hundreds of millions of GPUs and

computing systems around the world that

run CUDA. We are in every cloud. We're

in every computer company.

We serve just about every single

industry. The installed base of CUDA is

the reason why the flywheel is

accelerating. The install base is what

attracts developers who then creates new

algorithms that achieves a breakthrough.

For example, deep learning. There are so

many others. Those breakthroughs leads

to entirely new markets which builds new

ecosystems around them with other

companies that join which creates a

larger installed base. This flywheel,

this flywheel is now accelerating. The

number of downloads of Nvidia libraries

is incredibly accelerating. is at a very

large scale and growing faster than

ever. This flywheel is what makes this

computing platform able to sustain so

much applications, so many new

breakthroughs. But most importantly,

it also enables

these infrastructures to have

extraordinarily useful life. And the

reason for that is very obvious. There's

so many applications that you can run on

Nvidia CUDA. We support the entire every

single phase of the AI life cycle. We

address every single data processing

platform. We accelerate scientific

principled solvers of all different

kinds. And so the application reach is

so great that once you install Nvidia

GPUs, the useful life of it is

incredibly high. It is also one of the

reasons why Ampear that we shipped them

some six years ago the pricing of Ampear

in the cloud is going up. And so all of

that is made possible fundamentally

because the install base is high, the

flywheel is high, the developer reach is

great. And when all of that happens and

we continuously update our software,

the computing cost

declines. The combination of accelerated

computing speeding up applications

tremendously. Meanwhile, as we continue

to nurture and continue to update

software over its life, not only do you

get the first time pop, you get the

continuous cost reduction of accelerated

computing over time. And we're willing

to nurture, willing to support every

single one of these GPUs in the world

because they're all architecturally

compatible. We're willing to do so

because the install base is so large. If

we release a new optimization, it

benefits millions.

This applies to everybody in the world.

This combination of dynamics is what

makes the NVIDIA architecture expand its

reach, accelerating its growth, at the

same time driving down computing cost,

which ultimately

encourages new growth. So, CUDA is at

the center of it. But our journey that

could actually started 25 years ago.

GeForce.

I know how many of you grew up with

GeForce.

GeForce is Nvidia's greatest marketing

campaign.

We attract future customers starting

long before you could afford to pay for

it yourself. Your parents paid

Your parents paid your par your parents

paid for you to be Nvidia customers. And

every single year they paid up year

after year after year until someday you

became an amazing computer scientist and

became a proper customer, a proper

developer. But this is this is the house

that GeForce made 25 years ago. We

started our journey which led to CUDA.

25 years ago, we invented the

programmable shader. A perfectly

unobvious invention to make an

accelerator programmable. The world's

first programmable accelerator, the

pixel shader 25 years ago. That led us

to explore further and further 20 years

later, 5 years later, the invention of

CUDA. One of the biggest investments

that we made and we couldn't afford it

at the time. and it consumed the vast

majority of our company's profits was to

take CUDA on the backs of GeForce to

every single computer. We dedicated

ourselves to creating this platform

because we felt so much we felt so

strongly about its potential. But

ultimately the company's dedication to

it despite the hardships in the

beginning believing it every single day

for third for 13 generations or 20 years

we now have CUDA installed everywhere.

The pixel shader

led to of course the revolution of

GeForce.

And then 10 years ago, we introduced

about 10 years ago, what is it, eight

years ago, we introduced RTX,

a complete redesign of our architecture

for the modern era of computer graphics.

GeForce brought CUDA to the world.

GeForce

therefore enabled Alex Kruefky and Ilas

Susver and Jeff Hinton, Andrew Ang and

so many others to discover that the GPU

could be their friend in accelerating

deep learning. It started the big bang

of AI. 10 years ago, we decided that we

would fuse

programmable shading and introduce two

new ideas. ray tracing, hardware ray

tracing, which is incredibly hard to do.

And a new idea at the time, imagine

about 10 years ago, we thought that AI

would revolutionize computer graphics.

Just as GeForce brought AI to the world,

AI is now going to go back and

revolutionize how computer graphics is

done all together. Well, today I'm going

to show you something of the future.

This is our next generation of graphics

technology. We call it neuro rendering.

The fusion,

the fusion of 3D graphics and artificial

intelligence. This is DLSS 5. Take a

look at it.

Heat. Heat.

Heat. Heat.

Is that incredible?

Computer graphics comes to life. Now

what did we do? We fused

controllable 3D graphics. The ground

truth of virtual worlds, the structured

data, remember this word, the structured

data of virtual worlds, of gener

generated worlds. We combine 3D

graphics, structured data with

generative AI,

probabilistic computing. One of them is

completely predictive, the other one

probabilistic yet highly realistic. We

combine these two ideas. Combine these

two ideas controlled through structured

data controlled perfectly and yet

generating at the same time. And as a

result,

the content is beautiful, amazing, as

well as controllable. This concept of

fusing structured information and

generative AI will repeat itself in one

industry after another industry after

another industry. Structured data is the

foundation of trustworthy AI. Well,

this is going to scare you a little bit.

I'm going to flip the slide. and don't

gasp.

So, we're going to go through the

schematic for the rest of the time.

This is my best slide. Every time I I

asked my I asked the team, "What's my

best slide?" Repeatedly, this was it.

They say, "Don't do it, Jensen. Don't do

it." I said, 'N no, this these seats are

free

for some of you.

So this is your price of admission. So

this is this is structured data. You've

heard of it. SQL, Spark, Pandas, Velox,

some of these really really important

very large platforms. Snow, snow, uh,

snowflake, data bricks, EMR, Amazon EMR,

um, Azure, Fabric,

Google Cloud, BigQuery. All of these

platforms are processing data frames.

These data frames are giant spreadsheets

and they hold all of life's information.

This is the structured data, the ground

truth of business. This is the ground

truth of enterprise computing. Well, now

we're going to have AI use structured

data and we better accelerate the living

daylights out of it. It used to be okay

and we would, you know, of course we

would accelerate uh structured data so

that we could do more. We could do it

more cheaply. We could do it more

frequently per day and keep the company

running at a much more synchronized way.

However, in the future, what's going to

happen is these data structures are

going to be used by AI and AI is going

to be much much faster than us. Future

agents are going to use structured

databases as well. And then of course

the unstructured database, the

generative database. This database is

represents the vast majority of the

world. Vector databases, unstructured

data, PDFs, videos, speeches, all of the

world's information. About 90% of what's

generated every single year is

unstructured data. Until now, this data

has been completely useless to the

world. We read it, we put it into our

file system, and that's it.

Unfortunately, we can't query it. We

can't search for it. It's hard to do

that. And the reason for that is because

there's no easy indexing of unstructured

data. You have to understand its

meaning, its purpose. And so now we have

AI do that just as AI was able to solve

multi-modality

perception you can and understanding you

can use that same technology

multimodality perception and

understanding to go read a PDF to

understand its meaning and from that

meaning embedded into a larger structure

that we can search into we can query

into. NVIDIA created two foundational

libraries. Just like we created RTX for

3D graphics, we created QDF for data

frames, structured data. We created QVS

for vector stores, semantic data,

unstructured data, AI data. These two

platforms are going to be two of the

most important platforms in the future.

super excited to see its adoption

throughout the network, this complicated

network of the world's data processing

systems. And the reason for that is

because data processing has been around

a long time and therefore so many

different companies and platforms and

services. It has taken us a long time to

integrate deeply into this ecosystem.

I'm super proud of the work that we're

doing here. And then today we're

announcing several of them. IBM

the inventor of SQL

one of the most important domain

specific languages of all kind of all

time is accelerating Watson X data with

KUDF let's take a look at it

60 years ago IBM introduced the system

360

the first modern platform for

generalpurpose computing launching the

computing era then SQL a declarative

language to query data without requiring

the computer to be instructed step by

step

and the data warehouse. Each the

foundations of modern enterprise

computing. Today, IBM and NVIDIA are

reinventing data processing for the era

of AI by accelerating IBM Watson X. Data

SQL engines with NVIDIA GPU computing

libraries. Data is the ground truth that

gives AI context and meaning. AI needs

rapid access to massive data sets.

Today's CPU data processing systems

can't keep up. Nestle makes thousands of

supply chain decisions every day. Their

order to cache data mart aggregates

every supply order and delivery event

across global operations in 185

countries.

On CPUs, Nestle refreshed the data mart

a few times a day. With accelerated

Watson X data running on Nvidia GPUs,

Nestle can run the same workload five

times faster at 83% lower cost.

The next computing platform has arrived.

Accelerated computing for the era of AI.

NVIDIA accelerates data processing in

the cloud. We also accelerate data

processing on prem. As you know, Dell is

the worldleading computer systems maker

and they also are one of the world's

leading storage providers and they

worked with us to create the Dell AI

data platform that integrates QDF and

QVS to create an accelerated data

platform. well for the era of AI and uh

this is an example of what they did with

NT data huge speed up this is cloud

Google cloud and Google cloud as you

know we've been working with Google

cloud for a very long time we accelerate

Google's vertex AI we now accelerate

bigquery really important uh framework

and really important platform and this

is an example of our work together with

Snapchat where we reduce their cost of

computing by nearly 80%.

When you accelerate data processing,

when you accelerate computing, you get

the benefit of speed, you get the

benefit of scale, but most importantly,

you also get the benefit of cost. And so

all of those come together as one. It

was originally called Moore's law.

Moore's law was about getting

performance doubling every couple of

years. It's another way of saying so

long as the price remains about the same

and most computers remained about the

same, you're also getting twice the

performance every year or you're

reducing the cost of computing every

single year. Well, Moore's law has run

out of steam. We need a new approach.

Accelerated computing allows us to take

these giant leaps forward and as you

will see later because we continue to

optimize the algorithms

and Nvidia is an algorithm company. As

we continue to optimize the algorithms

and because our our reach is so large

and our install base is so large we can

reduce the computing cost increasing the

scale increasing the speed for everybody

continuously. This is Google cloud. You

could see this pattern I just mentioned.

I just wanted to show you three versions

of it. Nvidia built the accelerated

computing platform has a bunch of

libraries on top. I gave you three

examples. RTX is one of them. QDF is

another. KVS and we'll show you a few

more. These libraries sit on top of our

platform. But ultimately

we integrate into the world's cloud

services into the world's OEMs and

together and other platforms that I'll

show you together were able to reach the

world. This pattern Nvidia, Google

Cloud, Snapchat will repeat over and

over again. And kind of looks like this.

And so this is one example. Nvidia with

Google Cloud. We accelerate Vertex AI.

We accelerate Bitquery. We accelerate.

We're I'm super proud of the work that

we've done with Jackson XLA. We are

incredible on PyTorch. We're the only

accelerator in the world that's

incredible on PyTorch and incredible on

Jackson XLA. And the customers that we

support, the base 10s, the Crowd

Strikes, Puma, Salesforce, they're not

our customers, but they're customers,

developers of ours that we've integrated

the NVIDIA technologies into that we can

then land on the clouds.

Our relationship with cloud service

providers are essentially us bringing

customers to them. We integrate our

libraries, we accelerate workloads, and

we land those customers in the clouds.

And so, as you could see, most of our

cloud service providers love working

with us. And um they're always asking us

to land the next customer on their

cloud. And I just want to let you know

there are a lot of customers.

We're going to accelerate everybody. And

so, there will be lots and lots of

customers will be able to land in your

cloud. Just be patient with us. And so

this is Google Cloud. This is AWS. We've

been working with AWS a long time. And

one of the areas, one of the one of the

things I'm super excited about this year

is we're going to bring open AI to AWS.

And so it's going to drive enormous

consumption of cloud computing at AWS.

It's going to expand the reach, expand

the compute of open AI. And as you know,

they are completely compute constrained.

And so AWS, we accelerate EMR, we

accelerate SageMaker, we accelerate

Bedrock. NVIDIA's integrated really

deeply into AWS. They were our first

cloud partner,

Microsoft Azure.

NVIDIA's A100 supercomputer

um was the the first one we built was

for Nvidia. The first one we installed

was at Azure. And that led to the inter

the uh the big successful partnership

with open AI but we've been working with

Azure for quite a long time. We

accelerate Azure cloud now it's uh their

AI foundry we partner deeply with we

accelerate Bing search we work with them

on Azure regions. This is one of the

areas that is incredibly important as we

continue to expand AI throughout the

world. One of the capabilities that we

offer is confidential computing.

That in confidential computing, you want

to make sure that even the operator

cannot see your data. Even the operator

cannot touch or see your models.

confidential computing. Nvidia's GPUs is

the first ones in the world to do that.

It's now able to support confidential

computing and protected deployment of

these very valuable open AI models and

and anthropic models throughout clouds

and different regions and all because of

our conf confidential computing.

Confidential computing is super

important. And here's an example where

we have different customers that we work

with. Synopsis, a great partner of ours.

were accelerating all of their EDA and

CA workflows. And then we landed at

Microsoft Azure.

We were Oracle's first AI customer.

Most people would have thought we were

their first supplier. We were their

first supplier also, but we were their

first AI customer. I'm quite proud of

the fact that I explained AI clouds to

Oracle for the first time and we were

their first customer. Since then,

they've really taken off. We've landed a

whole bunch of our partners there. Core

Coher and Fireworks and of course very

famously open AAI

a great partnership with Core

Core. They're the world's first AI

native cloud. A company that was built

with only one singular purpose to

provision to host GPUs as the era of

accelerated computing showed up and to

host for AI clouds. They've got some

fantastic customers and they're growing

incredibly. One of the platforms that

I'm quite excited about is Palunteer and

Dell. The three of our companies have

made it possible to stand up a brand new

type of AI platform, the Palunteer

ontology platform and AI platform. And

we could stand up these platforms in any

country in any airgapped region

completely on prem, completely on site,

completely in the field. AI could be

deployed literally everywhere without

our confidential computing capability

without our ability to build the

endtoend system as well as offer the

entire

accelerated computing and AI stack from

data processing whether it's vectors or

structures all the way to AI it wouldn't

have been possible I wanted to show you

these examples

this is our special working relationship

with the world's cloud service providers

and many well all of them are here and I

get the benefit of seeing them during

boot tour and it's just so incredibly

exciting. I just want to thank all of

you for the hard work. What NVIDIA has

done is this and you're going to see

this theme over and over again.

Nvidia is vertically integrated the

world's first vertically integrated

but horizontally open company

and the reason that's necessary is very

simple. Accelerated

computing is not a chip problem.

Accelerated computing is not a systems

problem. Accelerated computing has a

missing word. We just never say it

anymore. Application acceleration.

You if I could make a computer run

everything faster, that's called a CPU.

But that's run out of steam. The only

way for us to accelerate applications

going forward and continue to bring

tremendous speed up, tremendous cost

reduction is through application or

domain specific acceleration. I dropped

that phrase in the in the front and

therefore it just became applica

accelerated computing and that is the

reason why Nvidia has to be library

after library, domain after domain,

vertical after vertical.

We are a vertically integrated computing

company. There is no other way. We have

to understand the applications. We have

to understand the domain. We have to

understand fundamentally the algorithms.

And we have to figure out how to deploy

the algorithm

in whatever scenario it wants to be

deployed. Whether it's a data center,

cloud, onrem, at the edge, or in a

robotic system. All of those computing

systems are different. And finally, the

systems and chips. We are vertically

integrated. What makes it incredibly

powerful and the reason why you saw all

the slides is because Nvidia is

horizontally open. We work and integrate

Nvidia's technology into whatever

platform you would like us to integrate

into. We offer you the software. We

offer you libraries. We integrate with

your technology so that we can bring

accelerated computing to everybody in

the world.

Well,

this GTC is really a great demonstration

of that. You know, most of the time,

most of the time you'll see me talk

about these verticals and I'll use some

examples, but in every single case,

whether it's automotive f by the way,

financial services, the largest

percentage of attendees at this GTC is

from the financial services industry.

I know. I I'm hoping it's developers,

not traders.

Guys,

here's here's

here's one thing I wanted to say. And so

in the audience represents Nvidia's

ecosystem upstream of our supply chain

and downstream of our supply chain. And

we work we think about our supply chain

upstream and downstream. And it's just

so exciting that

our entire upstream supply chain this

last year

irrespective of whether you're a 50 year

old company, we have 70 year old

companies. We have a 150 year old

company who are now part of Nvidia

supply chain and partnering with us

either upstream or downstream. And last

year

you had your record year, did you not?

Congratulations.

We're on to something here. This is the

beginning of something very, very big.

And so if you look at accelerated

computing, we've now set the computing

platform. But in order for us to

activate those computing platforms, we

need to have domain specific libraries

that solve very important problems in

each one of the verticals that we

address. You see us addressing every

single one of this. Autonomous vehicles,

our reach, our breadth, our impact.

Incredible. We have a track on that.

financial services. I just mentioned

algorithmic trading is going from

classical machine learning with human

feature engineering called quant the

quants did that to now supercomputers

studying massive amounts of data

discovering insight and discovering

patterns by itself and so this is going

through its deep learning and its

transformer moment healthcare is going

is going through their chap GPT moment

some really exciting work that we're

there we We have a great keynote track

here. We have a great keynote track.

Kimberly Pal is doing a great keynote

track um for healthcare. We're talking

about AI physics or AI biology for drug

discovery, AI agents for customer

service and support of diagnos diagnosis

and of course physical AI, robotic

systems. All these different vectors of

AI have different platforms that NVIDIA

provides. industrial we are completely

resetting and starting the largest

buildout of human history and most of

the world's industries building AI

factories building chip plants building

computer plants are represented here

today media and entertainment gaming of

course real time AI platform so that we

could translation and broadcast support

and live live games and live video

enormous amount of it will be augmented

with AI. We have a we have a platform

called hollow scan quantum there are 35

different companies here building with

us the next generation of quantum GPU

hybrid systems uh retail and CPG using

Nvidia for supply chain using creating a

gentic shopping systems

AI agents for customer support a lot of

work being done here $35 trillion

industry robotics $50 trillion industry

in manufacturing Nvidia has been working

in this area for a decade now building

three computers, the fundamental

computers necessary to build robotic

systems. We are integrated with working

with literally every single company that

we know of building robots. We have 110

robots here at the show. And then

telecommunications

about as large as the world's IT

industry about$2 trillion dollars. We

see of course base stations everywhere.

It's one of the world's infrastructures.

It was the infrastructure of the last

generation of computing. That

infrastructure is going to get

completely reinvented. And the reason

for that is very simple. That base

station which is

it does one thing which is base station

is going to be an AI infrastructure

platform in the future. AI will run at

the edge. And so lots of lots of great

um uh great uh discussion there. And our

platform there is called Aerial or AI

RAM. Big partnership with Nokia, big

partnership with T-Mobile and many

others.

At the core of our business,

everything that I just mentioned,

computing platforms, but very

importantly, our CUDA X libraries, our

CUDA X libraries is the algorithm, the

algorithms that Nvidia invents. We are

an algorithm company. That's what makes

us special. That what that's what makes

it possible for me to be able to go into

every single one of these industries,

imagine the future and have the world's

best computer scientists describe and

solve problems, refactor it, reexpress

it,

and turn it into a library. We have so

many I think we have at this show, we're

announcing a hundred 100 libraries,

something 70 libraries, maybe 40 models

and that's just at the show. We're

updating these all the time. We're

updating them all the time. The

libraries is the crown jewels of our

company. It is what makes it possible

for that platform, the computing

platform to be activated in service of

solving a problem, making impact. One of

the biggest, one of the most important

libraries that we ever created, coupn

CUDA deep neural networks. It completely

revolutionized artificial intelligence,

caused a big bang of modern AI. Let me

show you a short video about CUDA X.

20 years ago, we built CUDA, a single

architecture for accelerated computing.

Today, we've reinvented computing. A

thousand CUDA X libraries help

developers make breakthroughs in every

field of science and engineering.

CU opt for decision optimization.

CU litho for computational lithography.

CDSS for direct sparse solvers.

Coup equivariance for geometryaware

neural networks.

Aerial for AI ran.

Warp for differentiable physics.

pair of bricks for genomics.

At their foundation are algorithms and

they are beautiful.

Wow.

Heat. Heat.

Heat.

Heat.

Heat. Heat.

Heat.

Heat.

Everything you saw was a simulation.

Some of it was principled solvers,

fundamental physics solvers. Some of it

was AI surrogates, AI physical models

and some of it was physical AI robotics

models. Everything was simulated.

Nothing was animated. Nothing was

articulated. Everything was completely

simulated. That is what fundamentally

Nvidia does. It is through the

connection of understanding of the

algorithms with our computing platforms

that we're able to open up to unlock

these opportunities. Nvidia is a

vertically integrated computing company

with open

horizontal integration with the world.

So that's CUDA X. Well, just now you saw

a whole bunch of companies. You saw

Walmart and you know there's L'Oreal and

incredible companies established

companies JP Morgan and Ro and these are

companies in companies that have defined

society to today. Toyota is here. These

are some of the largest companies in the

world.

It is also true

that there's a whole bunch of companies

you've never heard of. These are

companies we call them AI natives. a

whole bunch of small companies. This the

list is gigantic. I can't I couldn't

this is just a little tiny tiny bit of

it. And um I I couldn't decide whether

to show you more or show you less. And

so I I made it so that you couldn't see

any

and and nobody's feelings are hurt.

However, inside this list are a bunch of

brand new companies. There are companies

like for example you might have heard a

couple of them open AAI anthropic but

there's a whole bunch of others there's

a whole bunch of others and they serve

different verticals

something happened in the last two years

particularly this last year we've been

working with the AI natives for a long

time and this last year it just

skyrocketed and I'll explain to you why

it happened these this industry has

skyrocketed $150 billion dollars of

investment into venture investment into

startups, the largest in human history.

This is also the first time that the

scale of the investments went from

millions of dollars, tens of millions of

dollars to hundreds of millions of

dollars and billions of dollars. And the

reason for that is this is the first

time in history that every single one of

these companies

needs compute and lots and lots of it.

They need tokens, lots and lots of it.

they're either need they're either going

to create and build and create tokens

and generate tokens or they're going to

integrate

add value tokens

that are available created by anthropic

and open AAI and others and so this

industry is different in so many

different ways but the one thing that is

very clear the impact that they're

making this the incredible value that

they're delivering already is quite

tangible AI natives

All because we reinvented computing.

Just like during the PC revolution, a

whole bunch of new companies were

created. Just as just as uh during the

internet revolution, a whole bunch of

companies were created and mobile cloud

a whole bunch of companies were created.

Each one of them had their own standards

and all we're talk about one of the

major standards is that just happened.

Incredibly important. And this

generation, we also have our own large

number of very, very special companies.

We reinvented computing. It stands to

reason there's going to be a whole new

crop of really important companies,

consequential companies for the future

of the world. The the Googles, the

Amazons, the Metas, consequential

companies that have come as a result of

the last computing platform shift. We

are now at the beginning of a new

platform shift. But what happened in the

last couple years? Well, we've been

watching, as you know, we've been

working on deep learning and working on

AI, the big bang of modern AI. We were

right there at the spot and we've been

advancing this field for quite some

time. But why the last two years? What

happened in the last two years? Well,

three things. Chat GPT of course started

the generative AI era. It's able to not

just understand, perceive, and

understand. It's able to also translate

and generate generation of unique

content. I showed you the fusion of

generative AI with computer graphics and

it brought computer graphics to life.

You guys just everybody in the world

should be using chat GPT. I know I use

it every single morning. Used it plenty

this morning. And so chat GPT was the

generative AI the era. The second by the

way generative generative computing

versus the way we used to do computing.

It's not it's generative AI is a

capability of software but it has

profoundly changed how computing is

done.

Computing used to be retrieval based now

it's generative. Keep that thought in

mind when I talk about certain things

and you'll realize why it is that

everything that we do is going to change

how computers are architected, how

computers are provided, how computers

are going to be built out and what is

the meaning of computing altogether

generative AI 2023 end of 22 2023 the

next reasoning AI 01

which and then took off with 03

reasoning allowed it to reflect, allows

it to think to itself, allowed it to

plan, break down, break down problems

and decompose a problem it couldn't

understand into steps or parts that it

could understand. It could ground itself

on research. 01 made generative AI

trustworthy and grounded on truth. That

caused Chad GPT to simply took off. And

that was a very, very big moment. the

amount of input tokens that was

necessary in order to produce and the

amount of output tokens it need it

generated in order to reason the model

was a little bit larger it you know of

course you could have much larger models

the model 01 was a little bit larger not

much larger but its input token usage

for context

and its output token for thinking

increased the amount of computation

tremendously then came quad code the

first agentic model. It was able to read

files, code, compile it, test it,

evaluate it, go back and iterate on it.

Cloud code has revolutionized software

engineering. As all of you know, 100% of

NVIDIA is using a combination of CL or

oftentimes all three of them. Cloud

code, codeex, and cursor all over

Nvidia. There's not one software

engineer today who is not assisted by

one or many AI agents helping them code.

Cloud code completely revolutionizes the

the new inflection and the for the first

time.

You don't ask a AI what,

where, when, how.

You ask it

create, do, build.

You ask it to use tools,

take your context, read files. It's able

to agentically break down a problem,

reason about it, reflect on it. It's

able to solve problems, and actually

perform tasks. An AI that was able to

perceive became an AI that could

generate. An AI that could generate

became an AI that could reason. An AI

that could reason now became an AI that

can actually do work. Very productive

work. The amount of computation in the

last two years, we know that everybody

in this room knows the computing demand

for NVIDIA GPU is off the charts. Spot

pricing is skyrocketing. You couldn't

find a GPU if you tried. And yet, in the

meantime, we're shipping GPUs out,

incredible amounts of it, and demand

just keeps on going up. There's a reason

for that. This fundamental inflection.

Finally, AI is able to do productive

work and therefore the inflection point

of inference has arrived.

AI now has to think. In order to think,

it has to inference. AI now has to do.

In order to do, it has to inference. AI

has to read. In order to do so, it has

to inference. It has to reason. It has

to inference. every part of AI

every time it has to think it has to

reason it has to do it has to generate

tokens it has to inference it's way past

training now it's in the in the field of

inference so the in the inference

inflection has arrived

at the time when the amount of tokens

the amount of compute necessary

increased by roughly 10,000 times now

when I combine these to the fact that

since in the last two years the

computing demand computing demand of the

work has gone up by 10,000 times and the

amount of usage

the amount of usage has probably gone up

by a hundred times.

People have heard me say I believe that

computing demand has increased by 1

million times in the last two years. It

is the feeling that we all have. It is

the feeling every startup has. It's the

feeling that OpenAI has. It's the

feeling that Anthropic has. If they

could just get more capacity, they could

generate more tokens. Their revenues

would go up. More people could use it.

The more advanced, the smarter the AI

could become. We are now at that

positive flywheel system. We have we

have reached that moment. The

inflection, the inference inflection has

arrived. Last year at this time, I said

that

where I stood at that moment in time, we

saw about

$500

billion dollars.

We saw$500 billion dollars

of very high confidence demand and

purchase orders

for Blackwell and Reuben through 2026.

I said that last year.

Now, I don't know if you guys feel the

same way, but $500 billion is an

enormous amount of revenue.

Not one impressed.

I know why you're not impressed. Because

all of you had record years.

Well, I'm here to tell you

that right now where I stand, a few

short months after GTCDC,

one year after last GTC, right here

where I stand,

I see through 2027

at least $1 trillion

Now, does it make any sense?

And that's what I'm going to spend the

rest of the time talking about. In fact,

we are going to be short. I am certain

computing demand will be much higher

than that. And there's a reason for

that. So, the first thing is

um we did a lot of work in the last

year. Of course, as you know, 2025 was

NVIDIA's year of inference. We wanted to

make sure that not only were we good at

training and post- training, that we

were incredibly good at every single

phase of AI so that the investments that

were made, investments made in our

infrastructure could scale out for as

long as they would like to use it. And

the useful life of Nvidia's

infrastructure would be long and

therefore the cost would be incredibly

low. The longer you could use it, the

lower the cost. There's no question in

my mind Nvidia systems are the lowest

cost infrastructure you could get for AI

infrastructure in the world. And so the

first part was last year was all about

AI for inference and it drove this

inflection point. Simultaneously

we were very pleased last year that

Anthropic has come to Nvidia that MSL

Meta SL has chosen Nvidia and meanwhile

meanwhile and as a collection as a group

this represents

onethird of the world's AI compute open-

source models open-source models have

reached near the frontier and it is

literally everywhere and Nvidia as you

know today we're the only platform in

the world today that runs every single

domain of AI

across every single one of these AI

models

in language and biology and computer

graphics computer vision and speech

proteins and chemicals robotics and

otherwise edge or cloud any language

NVIDIA's architecture is funible for all

of that and we're incredible for all of

that. That allows us to be the lowest

cost, the highest confidence platform

because when you're building these

systems, as I mentioned, a trillion

dollars is an enormous amount of

infrastructure. You have to have

complete confidence that the trillion

dollars you're putting down will be you

utilized, would be performant, would be

incredibly cost-effective, and have

useful life for as long as you could see

that infrastructure investment you could

make on Nvidia. You could make with

complete confidence.

We have now proven that it is the only

infrastructure in the world that you

could go anywhere in the world and build

with complete confidence. You want to

put it in any of the clouds, we're

delighted by that. You want to put it on

prem, we're happy about that. You want

to put it in any country, anywhere,

we're delighted to support you. We are

now

a computing platform that runs all of

AI. Now, our business

already starting to show that 60% of our

business is hyperscalers. The top five

hyperscalers.

However, even within that top five

hyperscalers, some of it is internal AI

consumption. The internal AI consumption

really important work like Rexus is

moving from recommener systems of tables

and collaborative filtering and content

filtering. It's moving towards deep

learning and large language models.

Search moving to deep learning large

language models. Almost all of these

different hypers scale workloads are now

moving shifting towards a workload that

Nvidia GPUs are incredibly good at. But

on top of that, because we work with

every AI lab, because we work with every

we accelerate a every AI model and

because we have a large ecosystem of AI

natives that we work with that we can

bring to the clouds that investment no

matter how large, no matter how quick

that compute will be consumed and that

represents 60% of our business. The

other 40% is just everywhere. Regional

clouds, sovereign clouds, enterprise,

industrial, robotics, edge, big systems,

supercomputing systems, small servers,

enterprise servers.

The number of systems, incredible.

The diversity of AI

is also its resilience.

The span of reach of AI is its

resilience. There is no question this is

not a one app technology. This is now

fundamental. This is absolutely a new

computing platform shift. Well, our job

is to continue to advance the technology

and one of the most important things

that I mentioned last year was last year

was our year of inference. We dedicated

everything. We took a giant chance and

reinvented while Hopper was at its prime

and it was just cooking. We decided that

the Hopper architecture the MVL link by

8 had to be taken to the next level. We

completely rearchitected the system,

disagregated the computing system alto

together and created MVLink 72. The way

that it's built, the way it's

manufactured, the way it's programmed

completely changed. Grace Blackwell

MVLink72 was a giant bet and it wasn't

easy for anybody and many of my partners

here in the room. I want to thank all of

you for the hard work that you guys did.

Thank you.

MVLink72

MV FP4 not just FP4 precision FP4 is a

whole different type of tensor core and

computational unit. We've demonstrated

now that we can inference NVFP4

without loss of precision but gigantic

boost in performance and energy

efficiency. We've also been able to use

MVFP4 for training. So MVLink72, MVFP4,

the invention of Dynamo, Tensor RTLM, a

whole bunch of new algorithms. We even

built a supercomputer to help us

optimize kernels and help us optimize

our complete stack. We call it DGX

cloud. We invested billions of dollars

of supercomputing capability help us

create the kernels, the software that

made inference possible. Well,

the results all came together and people

told people used to tell me but Jensen

inference is so easy. Inference is the

ultimate hard. Inference is ultimate

hard. It is also ultimate important

because it drives your revenues. And so

this is the outcome. This is from semi

analysis. This is the largest most

comprehensive sweep of AI that has AI

inference that has ever been done. And

what you see here on the left on on this

side on this side is tokens per watt.

Tokens per watt is important because

every data center every single factory

by definition is power constrained. A

one gawatt factory will never become

two. It's physically constrained the

laws of atoms, the laws of physicality.

And so that one gigawatt of data center

you want to drive the maximum number of

tokens which is the production the

product of that factory. So you want

that you want to be on top of that curve

as high as you want. This the x- axis is

the interactivity the speed of inference

the speed of each inference. The faster

you can inference,

the faster you could of course respond.

But very importantly, the faster you can

inference, the larger the models, the

more context you could process, the more

tokens you can think through. This axis

is the same as smartness of the AI. And

so this is the throughput of the AI.

This is the smartness of the AI. Notice

the smarter the AI, the lower your

throughput. Makes sense? you're thinking

longer. Okay? And so this axis is the

speed. And I'm going to come back to

this. This is important. This is where I

torture all of you. But it's too

important. Every CEO in the world you

watch, every CEO in the world will study

their business from now on in the way

I'm about to describe

because this is your token factory. This

is your AI factory. This is your

revenues. There's no question about that

going forward. And so this is the

throughput. This is the intelligence.

Better perf per watt for a given power

of data center. The more throughput, the

more tokens you could produce. On this

side is cost. Notice Nvidia is the

highest performance in the world. Nobody

would be surprised by that. They would

be surprised by the fact that in one

generation whereas Moore's law would

have given us through transistors 50%

two times

Moore's law would probably give us one

and a half times more performance. You

would have expected from Hopper H200 one

and a half times higher. Nobody would

have expected 35 times higher. I said

last year at this time that Nvidia's

Grace Blackwell NVLink 72 was 35 times

perf per watt. Nobody believed me. And

then semi-analysis came out and Dylan

Patel had a quote.

He accused me of sandbagging.

He accused me of sandbagging. He says,

"Jensen sandbagged. It's actually 50

times." And he's not wrong. He's not

wrong. And so our cost per token, yeah,

our cost per token is the lowest in the

world. You can't beat it.

I've said before, if you have the wrong

architecture, even if it's free, it's

not cheap enough. And the reason for

that is because no matter what happens,

you still have to build a gigawatt data

center. You still have to build build a

gigawatt factory. And that gigawatt

factory for 15 years advertised across

that gigawatt factory is about $40

billion. Even when you put nothing on

it, it's $40 billion in. You better make

for darn sure you put the best computer

system on that thing so that you could

have the best token cost. Nvidia's token

cost is world class.

basically untouchable at the moment. And

the reason that true is because of

extreme code design. And so I'm very

happy that he named us.

There was a monkey king,

token king.

Well, we take we take all of our

software as I as I told you, we

vertically integrate, but we

horizontally open. We're vertical

integration, horizontal open. We

integrate all of our software and all of

our technology, however we could package

it up and integrate it into the world's

inference service providers. And these

these companies are growing so fast.

They're growing so fast. Fireworks. Lynn

is here together. They're just growing

so incredibly fast. A hundred times in

the last year. They are token factories.

And the effectiveness, the performance

and the token cost production capability

for their factories is everything to

them. And this is what happened.

This is we updated their software, same

system.

And notice

their token speeds.

Incredible. The difference before before

Nvidia updated everything and all of our

algorithms and software and all the

technology that we bring to bear

about 700 tokens per second average went

to nearly 5,000 7 times higher. And so

this is the incredible power of extreme

code design. I mentioned earlier the

importance of factories. This is the

importance of factory. Your data center,

it used to be a data center for files.

It's now a factory to generate tokens.

Your factory is limited no matter what.

Everybody's looking for land, power, and

shell. Once you build it, you are power

limited. within that power limited

infrastructure, you better make for darn

sure that your inference because you

know inference is your workload and

tokens is your new commodity that

compute is your revenues that you want

to make sure that the architecture is as

optimized as you can in the future.

every single CSP,

every single computer company, every

single cloud company, every single AI

company, every single

company period are going to be thinking

about their token factory effectiveness.

This is your factory in the future. And

the reason why I know that is because

everybody in this room is powered by

intelligence. And in the future, that

intelligence will be augmented by

tokens. So, let me show you how we got

here.

On April 6th, 2016, a decade ago, we

introduced DGX1,

the world's first computer designed for

deep learning.

Eight Pascal GPUs connected with the

first generation NVLink.

170 teraflops in one computer. The

world's first computer designed for AI

researchers.

With Volulta, we introduced NVLink

switch. 16 GPUs connected with full

alltoall bandwidth operating as one

giant GPU. A giant step forward, but

model sizes continued to grow. The data

center needed to become a single unit of

computing. So, Melanox joined Nvidia.

In 2020, DGXA100 Super Pod became the

first GPU supercomput combining scale up

and scale out architecture.

NVL link 3 for scale up, connect X6 and

Quantum Infiniban for scale out.

Then Hopper, the first GPU with the FP8

Transformer engine that launched the

generative AI era. MVLink 4, Connect X7,

Bluefield 3 DPUs, second generation

quantum infiniband. It revolutionized

computing.

Blackwell redefined AI supercomputing

system architecture with NVLink 72. 72

GPUs connected by NVLink spine 130

terabytes per second of all to all

bandwidth.

Compute trays integrate Blackwell GPUs,

Grace CPUs, Connect X8, and Bluefield 3.

Scale Out runs over Spectrum 4 Ethernet.

With three scaling laws in full steam,

pre-training, post-training, and

inference, and now Agentic systems,

compute demand continues to grow

exponentially.

And now Vera Rubin

architected for every phase of Agentic

AI advancing every pillar of computing

including CPU storage networking and

security.

Vera Rubin Nvlink 72 3.6 exoflops of

compute 260 tab per second of alltoall

NVLink bandwidth the engine

supercharging the era of Agentic AI. The

Vera CPU rack designed for orchestration

and agentic workflows. The STX rack AI

native storage built with Bluefield 4.

Scale out with Spectrum X co-ackaged

optics increasing energy efficiency and

resiliency. And an incredible new

addition, the Gro 3 LPX rack. Tightly

connected to Vera Rubin, Gro's LPU's

massive onchip SRAMM, a token

accelerator to the already incredibly

fast Vera Rubin. Together, 35 times more

throughput per megawatt. The new Vera

Rubin platform. Seven chips, five rack

scale computers, one revolutionary AI

supercomput for agentic AI.

40 million times more compute in just 10

years.

Now, in the in the good old days when I

would say hopper, I would hold up a

chip.

That's just adorable.

This is Vera Rubin. When we think ver

when we when we think Vera Rubin, we

think the entire system vertically

integrated

completely with software

extended end to end optimized as one

giant system. The reason why it's

designed for agentic systems is very

clear because agents of course the most

important workload is it's thinking the

large language model. The large language

models are going to larger and larger

and larger. It's going to generate more

and more tokens more quickly so it could

think more quickly. But it also has to

access memory. It's going to pound on

memory really hard. KV cache structured

data QDF unstructured data QVS. It's

going to be pounding on the me on the

storage system really really hard which

is the reason why we reinvented the

storage system. It is also going to use

tools and unlike humans that are more

tolerant to slower computers.

AI wants the tools to be as fast as

possible. These tools web browsers in

the future they could also be virtual

PCs in the cloud. Those PCs have to be

and those computers have to be as fast

as possible. We created a brand new CPU.

A brand new CPU that's designed for

extremely high singlethreaded

performance,

incredibly

high data output, incredibly good at

data processing, and extreme energy

efficiency. It is the only data center

CPU in the world that uses LPDDR5,

LPDDR5 and incredible single thread

performance and performance per watt

that is unrivaled.

And so that's we built that so that it

could go along with the rest of these

racks for agentic processing. And so

here it is. This is the Grace Blackwell.

Oh no, Vera Rubin. Where is it? Here it

is. Okay, so this is the Vera Rubin

system. Notice since the last time 100%

liquid cooled. All of the cables gone.

What used to take what used to take

2 days to install now takes two hours.

Incredible. And so the manufacturing

cycle time going to dramatically reduce.

This is also a supercomput that is

cooled by it's cooled by hot water 45°

which takes the pressure off of the data

center takes all of that cost and all of

that energy that's used to cool the data

center and makes it available for the

system. This is the secret sauce. It is

the only we're the only company in the

world that has today built the sixth

sixth generation scaleup switching

system. This is not Ethernet. This is

not Infiniban. This is MVLink. This is

the sixth generation MVLink. This is

insanely hard to do. Well, it is

insanely hard to do. Period. And I'm

just super proud of the team. MVLink

completely cooled. This is the brand new

Gro system. And I'll show you a little

bit more about it. this system.

Eight GU chips. This is the LP30. The

world's never seen it. Anything that the

world's ever seen is V1. This is third

generation.

And we're in volume production now. And

I'll show you more about that in just a

second. The world's first

CPO

Spectrum X switch. This is also in full

production. Co-packaged optics. Optics

comes directly onto this chip,

interfaces directly to silicon.

Electrons gets translated to photons and

it gets directly directly connected to

this chip. We invented the process

technology with TSMC. We're the only one

in production with it today. It's called

coupe. It's completely revolutionary.

Nvidia is in full production with

Spectrum X.

This is the Vera system. Twice the

performance per watt of any any CPUs in

the world today. It is also in

production. Well, you know, we never we

never thought we would be selling CPUs

standalone. Um, we are selling a lot of

CPU standalone. This is already for sure

going to be a multi-billion dollar

business for us. So, I'm very very

pleased with our CPU architects. We

designed a revolutionary CPU and this is

the CX9

powered with Vera CPU, the Bluefield 4

STX, our new storage platform. Okay, so

these are the four these are the the the

racks and it's connected

each one of these racks, the MVL link

rack.

This is I've shown you guys this before.

It's a super heavy and seems to get

heavier every year.

because I think there's just more cables

in there every year. And so, so this is

the MVLink rack. We've also taken this

technology because it it is so

efficient to create a data center with

these cabling systems, structured

cables. So, we decided to do that for

Ethernet. So, this is Ethernet, 256

liquid cooled nodes in one rack. And it

is also connected with these incredible

connectors.

You guys want to see um

Reuben Ultra.

So this is the Reuben Ultra compute

node. Unlike Reuben that slides in

horizontally, Ruben Ultra goes into a

whole new rack. It's called Kyber that

enables us to connect 144 GPUs in one

MVLink domain. And so the Kyber rack,

this I I could lift it, I'm sure, but I

won't.

It's quite heavy. This This is one

compute node, and it slides into the

Kyber rack vertically.

This is where it connects into. This is

the midplane. The Kyber racks, those

four top MVLink connectors slide in and

connect into this. And this becomes one

of the nodes.

And each one of these racks is a

different compute node. And this is the

amazing part. This is the midplane.

And the back of the midplane, instead of

the cabling system,

which has its limits in terms of how far

we could drive cables, copper cables, we

now have this system to connect 144

GPUs. This is the new MVLink. This sits

also vertically and it con connects into

the midplanes on the back. Compute in

the front, MVLink switches in the back.

One giant computer. Okay. So that is

Reuben Ultra

as I mentioned. as I mentioned.

How about we t take this back down?

I need the rest of my slides.

>> Oh, it's coming down. Okay. Thank you,

Janine.

This is what happens when you This is

what happens when you don't practice.

Okay. All right. So, um you saw you

Take your time. Just don't get hurt.

You saw you saw this slide. You know,

only at Nvidia's keynote will you see

last year's slide presented again. And

the reason for that is I just want to

let you know that last year I told you

something very, very important. And it's

so important. It's worthwhile to tell

you again.

This is probably the single most

important chart for the future of AI

factories. And every CEO, every CEO in

the world will be tracking it. We'll be

studying it very deeply. It's much much

more complicated than this. It's

multi-dimensional.

But you will be studying the throughput

and this token speed of your AI

factories. The throughput, token speed

at ISO power because that's all the

power you have. Throughput and token

speed for your factories forever. And

that that analysis is going to lead

directly to your revenues. What you do

this year will show up precisely next

year as your revenues. And this chart is

what it's all about. And I said on the

vertical axis, on the vertical axis,

thank you guys. On the vertical axis is

throughput. On the horizontal axis is

token rate. Today I'm going to show you

this

because we're able because we're now

able to increase the token speed and

because model sizes are increasing

because the token length the context

length depending on the different grades

of a different application use case

continues to grow from maybe a 100,000

tokens input length to maybe millions.

the token input length is growing and

also the output token length is growing.

And so all of these play into ultimately

the marketing and the pricing of future

tokens. Tokens are the new commodity and

like all commodities once it reaches an

inflection once it becomes mature or

becomes maturing it will segment into

different parts. The high throughput

low speed could be used for the free

tier. The next tier could be the medium

tier. Larger model, maybe higher speed

for sure, larger input context length.

That translates to a different price

point. You could see from all the

different services, this one is free.

It's a free tier. The first tier could

be $3 per million tokens. The next tier

could be $6 per million tokens. You

would like to be able to keep pushing

this boundary because the larger the

model smarter, the more input token

context length, more relevant, the

higher the speed, the long the more you

can think and iterate smarter AI models.

So this is about smarter AI models. And

when you have smarter AI models, each

one of these clicks allows you to

increase the price. So this is $45. And

maybe one day there'll be a premium

model that allows you a premium service

that allows you to generate token speeds

that are incredibly high because you're

in a critical path or maybe you're doing

really long research and $150 per

million tokens is just not a thing. So

let's translate that. Suppose you were

to use 50 million tokens per day as a

researcher at $150 per million tokens.

As it turns out, as a research team,

that's not even a thing. So, we believe

that this is the future. This is where

AI wants to go. This is where it is

today.

It had to start here to establish the

value and establish it usefulness and

get better and better and better. In the

future, you're going to see most

services encompass encompass all of

that. This is Hopper.

Hopper started and I moved it moved the

chart. This is 50. This is 100. Hopper

looks like this. And you would have

expected Hopper the next generation to

be higher, but nobody would have

expected it to be that much higher. This

is Grace Blackwell. What Grace Blackwell

did is at your free tier increase your

throughput tremendously.

However,

where you mostly monetize your service,

it increased your throughput by 35

times. This is no different than any

product that every company makes. The

higher the tier, the higher the quality,

the higher the performance, the lower

the volume, the lower the capacity. And

so it is no different than any other

business in the world. And so now we're

able to increase this tier by 35x.

And we introduced a whole new tier.

This this is the benefit of Grace

Blackwell. A huge jump over Hopper.

Well, this is what we're doing with

Okay. So, this is Grace Blackwell. Okay.

Let me just reset reset this.

And this is Vera Rubin.

Okay.

Now, just think just think what just

happened at every single tier. At every

single tier, at every single tier, we

increase the throughput. And at the tier

that where your highest ASP and your

most valuable segment, we increased it

by 10x.

That is the hard work. This is

incredibly hard to do out here. This is

the benefit of EVL 72. This is the

benefit of extremely low latency. This

is the benefit of extreme code design

that we could shift the entire area up.

Now, what does it mean from a customer

perspective in the end? Suppose I were

to take all of that and I just, you

know, multiply it against suppose I took

25% of my power, used it in free tier,

25% of my power in the medium tier, 25%

of my power in the high tier and 25% of

my power in the premium tier. My data

center only has a gigawatt.

And so I get to decide how I want to

distribute. The free tier allows me to

attract more customers.

This allows me to serve my most valuable

customers.

And the combination, the product of all

that allows you basically your revenues,

the revenues you can generate, assuming

this simplistic example, allows

Blackwell to generate five times more

revenues.

Vera Rubin to generate five times. Yeah.

So if you're a Reuben, you should get

there as soon as you can. And the reason

for that is because your your cost of

tokens goes down and your throughput

goes up now. But we want even more. We

want even more. And so let me just show

you back to this. This is as you as I as

I told you this throughput requires a

ton of flops. This latency, this

interactivity requires enormous amount

of bandwidth. Computers don't like

extreme amount of flops, extreme amount

of bandwidth because there's only so

much surface area for chips that any

systems has. And so optimizing for high

throughput and optimizing for low

latency are in fact enemies of each

other. And so this is what happened when

we combined with rock. Okay. And so we

we acquired the team that worked on the

Gro chips and licensed the technology

and we've been working together now to

integrate the system. This is what that

looks like. So at the most valuable tier

at the most valuable tier we're now

going to increase performance by 35x.

Now this very simple chart revealed to

you exactly the reason why Nvidia is so

strong in the vast majority of the

workloads so far. And the reason for

that is because up in this area

throughput matters so much. MVLink 72 is

so gamechanging. It is exactly the right

architecture and it's even hard to beat

even as you add Grock to it. However,

if you extended this chart way out here

and you said you wanted to have services

that delivers not 400 tokens per second

but a thousand tokens per second, all of

a sudden MVLink72 runs out of steam and

it simply can't get there. We just don't

have enough bandwidth. And so this is

where Grock comes in and this is what

happens when we push that out. So it

goes out beyond Thank you.

goes out beyond even the limits of what

MVLink72 can do. And if you were to do

that, translate that into revenues

relative to Blackwell Vera Rubin is 5x.

If most of your workload is high

throughput, I would stick with just 100%

Vera Rubin. If a lot of your workload

wants to be coding and very high valued

engineering token generation, I would

add Grock to it. I would add Grock to

maybe 25% of my total data center. The

rest of my data center is all 100% Vera

Rubin. And so that gives you a sense of

how you would add Grock to Vera Rubin

and extend its performance and extend

its value even more. This is what

happens.

Ver this is a contrast. The reason why

the reason why Grock was so attractive

to me is because their computing system

a deterministic data flow processor it

is statically compiled. It is compiler

scheduled meaning the compiler figures

out when the data when to do the compute

the the compute and the data arrives at

the same time. All of that is done

statically in advance

and scheduled completely in software.

There's no dynamic scheduling.

The architecture is designed with

massive amounts of SRAMM. It is designed

just for inference. This one workload.

Now, this one workload, as it turns out,

is the workload of AI factories. And as

the world continues to increase the

amount of high-speed tokens it wants to

generate with super smart tokens it

wants to generate, the value of this

integration is going to get even higher.

And so these are two extreme processors.

You could see one chip 500 megabytes,

one Vera Ruben chip, one Ruben chip 288

gigabytes.

It would take a lot of rock chips to be

able to hold the parameter size of

Reuben as well as all of the context

that has to go the KV cache that has to

go along with it. So that limited

Grock's ability to really reach the

mainstream to really take off until we

had a great idea. What if we

disagregated inference altogether with a

piece of software called Dynamo? What if

we rearchitected the way that inference

is done in the pipeline? so that we

could put the work that makes perfect

sense on Vera Rubin and then offload the

decode generation the low latency the

bandwidth limited challenged part of the

workload for Grock and so we united

unified

two processors of extreme differences

one for high throughput one for low

latency it still doesn't change the fact

that we need a lot of memory and so

Grock we're just going

add a whole bunch of Grock chips which

expands the amount of memory it has and

so if you could just imagine

out of a trillion parameter model we

have to store all of that in gro chips

however it sits next to Nvidia Vera

Rubin where we could we could hold the

massive amounts of KV cache that's

necessary in processing all of these

agentic AI systems it's based upon this

idea of this aggregated inference we do

the prefill that's the easy part but we

also tightly integrate the decode so the

attention part of decode is done on

Nvidia's Vera Rubin which needs a lot of

math and the feed forward network part

of it the decode part is done uh the

token generation part is done on Vera

Rubin on the uh on the groip the two of

them working tightly coupled together

over today Ethernet with a special mode

to reduce its latency by about half. And

so that capability allows us to

integrate these two systems. We run

Dynamo, this incredible operating system

for AI factories on top of it. And you

get 35 times increase. 35 times

increase. Not to mention additional new

tiers of inference performance for token

generation the world's never seen. So

this is it. This is Grock.

the Vera Rubin systems including Grock.

I want to thank Samsung uh who

manufactures the Gro LP30 chip for us

and they're cranking as hard as they

can. I really appreciate appreciate you

guys. We're in production with the Gro

chip and uh you know we'll ship it in

the second half probably about Q3 time

frame. Okay.

Grock LPX

Vera Rubin you know it's kind of hard

it's kind of hard to imagine any more

customers

you know and and uh the the really great

thing is is um Grace Blackwell early

sampling of it was really complicated

because of coming together of Envy Link

72 but the sampling of Vera Rubin is

just going incredibly well and in fact

Satia I think texted out already that

the first Vera Rubin rack is already up

and running at Microsoft Azure and so

I'm super excited for them. We're just

going to keep cranking these things out.

We have now set up a supply chain that

could manufacture thousands a week of

these systems essentially multi-

gigawatts of AI factories per month

inside our supply chain. And so we're

going to crank out these these Vera

Rubin racks while we're cranking out the

GB300 racks. We are in full production.

The Vera CPUs

incredibly successful. And the reason

for that is because AI needs CPUs for

tool use and Vera CPU was designed just

perfectly for that sweet spot.

Incredible for the next generation of

data processing. Vera CPU is ideal. the

Vera CPU plus blue plus CX9 connected

into the Bluefield fourstack

100%

100% of the world's storage industry is

joining us on this system and the reason

for that is because they see exactly the

same thing. The storage system is going

to get pounded. It's going to get

pounded because we used to have humans

using the storage systems. We used to

have humans using SQL. Now we're going

to have AIS using these storage systems

and it's going to store QDF accelerated

storage, QVS accelerated storage as well

as very importantly KV caching. Okay, so

this is the Vera Rubin system. Now

what's amazing is this. in just two

years time in a one gigawatt factory in

just two years time in one gigawatt

factory using the mathematics that I

showed you earlier whereas Moore's law

would have given us a couple of steps we

would have you know x factored the

number of transistors we would have x

factored the number of flops we would

have x factored the number of amount of

bandwidth but with this architecture

we're going to take our token generation

ation speed token generation rate from 2

million to 700 million 350 times

increase.

This is this is the power of extreme

code design. This is what I mean when we

integrate and optimize vertically but

then we open it horizontally for

everybody to enjoy. This is our road

map. Very quickly,

Blackwell is here, the Oberon system. In

the case of Reuben, we have the Oberon

system. We're always backwards

compatible. So that if you wanted to not

change anything and just keep on moving

through with the new architecture, you

could do so.

The old the standard um rack system

Oberon still available. Oberon is copper

scale up. And with Oberon, we could also

use optical scale out or excuse me,

optical scale up to expand to MVLink

576.

Okay. And so there's a lot of

conversation about is Nvidia going to

copper scale up or optical scale up.

We're going to do both.

So, we're going to have MVLink 144 with

Kyber and then with Operon uh Opteron

Oberon, we're going to MVLink72

plus Optical to get to MVLink 576.

The next generation of Reuben with

Reuben Ultra. We have the Reuben Ultra

chip which is coming which is imp taping

out and we have a brand new chip LP35.

LP35 will for the first time incorporate

Nvidia's MVFP4 computing structure give

you another few X X factor speed up.

Okay. And so this is Oberon MVLink 72

optical scale up and it uses Spectrum 6

the world's first co-ackaged optical and

um all of this is in production. The

next generation from here

is Fman. Fineman has a new GPU of

course. It also has a new LPU

LP40.

Big step up. Incredible. Incredible new

technology. Now

uniting the scale of Nvidia and the Gro

team building together LP40. It's going

to be incredible. a brand new CPU called

Rosa,

short for Roslin. Bluefield 5, which

connects the next CPU with the next

Superneck

CX10.

We will have Kyber

which is copper scale up. We will also

have Kyber

CPO scale up. So for the first time we

will scale up with both copper and

co-ackage optics. Okay. And so a lot of

people have been asking you know Jensen

are is copper going to still be

important? The answer is yes.

Jensen are you going to scale up

optical? Yes.

Are you going to scale out optical?

Yes.

And so for everybody who is in our

ecosystem, we need a lot more capacity

and that's really the key. We need a lot

more capacity for cop for copper. We

need a lot more capacity for optics. We

need a lot more capacity for CPO and

that's the reason why we've been working

with all of you to lay the foundation

for this level of growth. And so Fman

will have all of that. Let me see if I

uh missed everything. That's it. every

single year. Brand new architecture.

Very quick.

Very quickly, Nvidia went from a chip

company to a AI factory company or AI

infrastructure company, AI computing

company. These systems

and now we're building entire AI

factories. There's so much power

that is squandered in these AI

factories. We want to make sure that

these AI factories come together

designed in the best possible way. Most

of these components never meet each

other. Most of most of us technology

vendors now we all know each other but

in the past we never met each other

until the data center. That can't

happen. We're building super complex

systems and so we have to meet each

other virtually somewhere else and so we

created Omniverse and the Omniverse DSX

world a platform where all of us can

meet and design these gigafactories the

giga you know gigawatt AI factories

virtually in system. We have simulation

systems for the racks for mechanical,

thermal, electrical, networking. Those

simulation systems integrated into all

of our ecosystem partners of incredible

tools companies. We also operated,

connected to the grid so that we could

interact with each other, send each

other information so that we could

adjust

grid power and data center power

accordingly, saving energy. And then

inside the data center using Max Q so

that we could adjust the system

dynamically across power and cooling and

all of the different technologies we all

work on together so that we leave no

power squandered

so that we run at the most optimal rate

to deliver enormous amount of token

throughput. There's no question in my

mind there's a factor of two in here and

a factor of two at the scale we're

talking about is gigantic. We call this

the NVIDIA DXX platform. And just as all

of our platforms, there's the hardware

layer, there's the library layer, and

there's the ecosystem layer. It's

exactly the same way. Let's show it to

you.

The greatest infrastructure buildout in

history is underway.

The world is racing to build chip system

and AI factories. And every month of

delay costs billions in lost revenues.

AI factory revenues are equal to tokens

per watt. So with power constraints,

every unused watt is revenue lost.

NVIDIA DSX is an Omniverse digital twin

blueprint for designing and operating AI

factories for maximum token throughput,

resilience, and energy efficiency.

Developers connect through several APIs.

DSX SIM for physical, electrical,

thermal and network simulation, DSX

exchange for AI factory operational

data, DSX Flex for secure dynamic power

management between the grid and DSX Max

Q to dynamically maximize token

throughput.

It starts with SIM ready assets from

NVIDIA and equipment manufacturers

managed by PTC windshield PLM.

Then modelbased systems engineering is

done in DASO systems 3D experience.

Jacobs brings the data into their custom

Omniverse app to finalize design.

It's tested with leading simulation

tools using Seaman's Star CCCM Plus for

external thermals,

Cadence Reality for internal,

EAP for electrical, and NVIDIA's network

simulator DSX Air

and virtually commission through Procore

to ensure accelerated construction time.

When the site goes live, the digital

twin becomes the operator. AI agents

work with DSX Max Q to dynamically

orchestrate infrastructure.

Fedra's agent overseas cooling and

electrical systems, sending signals to

Max Q, which continuously optimizes

compute throughput and energy

efficiency.

Emerald AI agents interpret live grid

demand and stress signals and adjust

power dynamically.

With DSX, Nvidia and our ecosystem of

partners are racing to build AI

infrastructure around the world,

ensuring extreme resiliency, efficiency,

and throughput.

It's incredible, right? Well,

om Omniverse Omniverse was designed to

hold the world's digital twin starting

from the earth and it's going to hold

digital twins of all sizes. And so we

have just such a great ecosystem of

partners. I want to thank all of you.

All of these companies are brand new to

our world. We didn't know many of you

just a couple years ago. And now we're

working so close together to work on and

build together the largest computer the

world's ever seen and also to do it at

planetary scale. So NVIDIA DSX is our

new AI factory platform.

I'll spend very little time on this at

this time. However, we're going to

space. We've already been out in space.

uh Thor is radiation approved and uh

we're in satellites. You do imaging from

the from satellites. In the future,

we'll also build data centers in the in

the in the in space. Uh obviously very

complicated to do. So we have we're

working with our partners on a new

computer called Vera Rubin Space 1 and

it's going to go out to space and start

data centers out out in space. Now, of

course, in space, there's no conduction,

there's no convection, there's just

radiation. And so, we have to figure out

how to um uh cool these systems uh out

in space. But, we've got lots of great

engineers working on it. Let me talk to

you about something new.

So, so um

uh Peter Steinberger is here and um uh

he he wrote a piece of software. It's

called Open Claw and and um I don't know

if he realized uh how successful it was

going to be. Um but the importance is

profound. Open Claw is the number one.

It's the most popular opensource project

in the history of humanity and it did so

in just a few weeks.

It exceeded it exceeded what Linux did

in 30 years. And it's that important. It

is that important. It will do well.

Uh this is all you do. Okay? We're

announcing our support of it. Uh let me

let me just quickly go through this.

this. I want to show you a couple

things. You simply type this, you type

it this into a into a console and um it

goes out, it finds open claw, it

downloads it, it builds you an AI agent,

and then you could tell it whatever else

you need to do. Okay, so let's take a

look.

An open source project just dropped.

>> Andre Carpathy has just launched

something called research is a huge

deal.

>> You give an AI agent a task, go to

sleep, it runs 100 experiments

overnight, keeping what works and

killing what doesn't.

I really love what my stuff enables that

person to do. And he had like one guy,

he told me like he installed it as a

60-year-old dad and like they made beer,

connected the machine via Bluetooth to

open claw. And then we automated

everything including the whole website

for people to order lobster.

Hundreds of people are queuing up for

lobsters in s openclaw.

>> Open claw.

>> You want to build open claw with open

claw.

>> Everyone is talking about open claw. But

what is open claw?

>> Believe it or not, there's already a

claw con.

Incredible. Incredible. Now, um I

illustrated effectively what open claw

is in this way and so all of you can

understand it. But let's just think what

happened. What is open claw? It connects

it's an a it's a system. It calls and

connects to large language models. So

the first thing it has it has resources

that it manages. It manage it could

access tools. It could access file

systems. It could access large language

models. It It's able to do scheduling.

It's able to do cron jobs. It's able to

um uh decompose a problem that a prompt

that you gave it into step by step by

step. It could spawn off and call upon

other sub aents.

It has IO. You could talk to it in any

modality you want. You could wave at it

and it understands you. You could talk

to any modality you want. It sends you

messages, it texts you, sends you email.

So, it's got IO.

Um, what else does it have? Well, based

on that, you could you could say in fact

it's an operating system. I've just used

the same syntax that I would describe an

operating system. Art

openclaw has open sourced essentially

the operating system of agent computers.

It is no different than how Windows made

it possible for us to create personal

computers. Now open claw has made it

possible for us to create personal

agents.

The implication is incredible. The

implication is incredible. First of all,

the adoption says something you know all

in itself. However, the most important

thing is this. Every single company now

realize every single company, every

single software company, every single

technology company for the CEOs, the

question is what's your open claw

strategy?

Just as we need to all have a Linux

strategy, we all needed to have a HTTP

HTML strategy which started the

internet. We all needed to have a

Kubernetes strategy which made it

possible for mobile cloud to happen.

Every company in the world today needs

to have an open claw strategy and a

gentic system strategy. This is the new

computer. Now this is just the exciting

part. This is enterprise IT before

openclaw you know and and I mentioned

earlier the way enterprise IT works and

the the reason these reason why it's

called data centers is because these

large rooms these large buildings held

data held the files of people the

structured data of business. It would

pass through software that has tools and

you know systems of records and all

kinds of workflow that's codified into

it and that turns into tools that humans

would use

digital workers would use. That is the

old IT industry software companies

creating tools saving files and of

course gsis consultants that help

companies figure out how to use these

tools and integrate these tools. These

in these tools are incredibly valuable

for governance and security and privacy

and compliance and all of that's

continues to be true.

It's just that post open clock post

agentic this is what it's going to look

like. This is the extraordinary part.

Every single IT company, every single

company, every SAS company,

every SAS company will become a

a gas company.

No question about it. Every single SAS

company will become a gas company, an

agentic as a service company. And what's

amazing is this. You now open claw gave

us gave the industry exactly what it

needed at exactly the time.

Just as Linux gave the industry exactly

what it needed at exactly the time just

as Kubernetes showed up at exactly the

right time just as HTML showed up it

made it possible for the entire industry

to grab onto this open-source stack and

go do something with it. There's just

one catch.

Agentic systems

in the corporate network can have access

to sensitive information. It can execute

code and it can communicate externally.

Just say that out loud. Okay, think

about it. Access sensitive information,

execute code, communicate externally.

You could of course access employee

information,

access supply chain, access finance

information, sensitive information and

send it out, communicate externally.

Obviously,

this can't possibly be allowed. And so,

what we did was we worked with Peter. We

took some of the world's best security

and computing experts and we worked with

Peter to make open claw

open claw enterprise

secure and enterprise private capable.

And we call that

this is our Nvidia open claw reference

for open nemo claw which is a reference

for openclaw and it has all these

agentic AI toolkits and the first part

of it is technology we call open shell

that has now been integrated into open

claw now it's enterprise ready this

stack this stack with a reference design

we call Nemo cloud neoclaw

Okay, with a reference stack we call

Nemo clock. You could download it, play

with it, and you could connect to it the

policy engine of all of the SAS

companies in the world. And your policy

engines are super important, super

valuable. So the policy engines could be

connected Nemo Claw or Open Claw with

Open Shell would be able to execute that

policy engine. It has a polic

guard rail. It has a privacy router and

as a result we could protect and keep

the the clause from executing inside our

company and do it safely. We also added

several things to the agent system and

one of the most important things you

want to do with your own

claw custom claws is so that you can

have your custom models and this is

Nvidia's open model initiative. We are

now at the frontier of every single

domain of AI models. Whether it's

Neimotron, Cosmos, World Foundation

model, Groot, artificial general

robotics, human or robotics models,

Alpamo for autonomous vehicle, Bioneo

for digital biology,

Earth 2 for AI physics. We are at the

frontier on every single one. Take a

look.

The world is diverse. No single model

can serve every industry.

Open Models is one of the largest and

most diverse AI ecosystems in the world.

Nearly 3 million open models across

language, vision, biology, physics, and

autonomous systems enable AI builds for

specialized domains. NVIDIA is one of

the largest contributors to open-source

AI. We build and release six families of

open frontier models, plus the training

data, recipes, and frameworks to help

developers customize and adopt new

leaderboard topping models are launching

for every family. At the core, Neotron

reasoning models for language, visual

understanding, rag,

safety,

and speech.

>> Can you hear me now? Hello. Yes, I can

hear you now.

>> Cosmos Frontier models for physical AI

world generation and understanding.

Alpayo, the world's first thinking and

reasoning autonomous vehicle AI

group foundation models for general

purpose robots. Bioneo open models for

biology, chemistry, and molecular

design.

Earth 2 models for weather and climate

forecasting rooted in AI physics.

NVIDIA open models give researchers and

developers the foundation to build and

deploy AI for their own specialized

domains.

Our models our mo thank you

our models are valuable to all of you

because number one it's on the top of

the leaderboard. It's world class. But

most importantly, it's because we are

not going to give up working on it.

We're going to keep on working on it

every single day. Neotron 3 is going to

be followed by Neotron 4. Cosmos one was

followed by Cosmos 2. Groot Groot at

generation 2. Each and one of these

we're going to continue to advance these

models. vertical integration,

horizontal openness, so that we can

enable everybody to join the AI

revolution, number one on leaderboard

across research and voice and world

models and artificial general robotics

and self-driving cars and reasoning and

of course one of the most important one.

This is Neotron 3 in

Open Claw. This is Neimotron 3 and Open

Claw. And look at the top three. There

are the three best models in the world.

Okay. So, we are at the frontier.

It is also true. It is also true that we

want to create the foundation model so

that all of you could fine-tune it and

post-train it into exactly the

intelligence you need. This is Neotron 3

Ultra. It is going to be the best base

model the world's ever created. This

allows us to help every country build

their sovereign AI. And we're working

with so many different companies out

there. And one of the most exciting

things that we're doing today, I'm

announcing today is a Neotron coalition.

We are so dedicated to this. We have

invested billions of dollars of AI

infrastructure so that we could develop

the core engines for AI that's necessary

for all the libraries of inference and

so on. But also to create the AI models

to activate every single industry in the

world. Large language models is really

important. Of course, it's important.

How could how could human intelligence

not be? However, in different industries

around the world, in different countries

around the world, you need to have the

ability to customize your own models and

the domains of the domain of the domain

of the models is radically different

from biology to physics to self-driving

cars to general robotics to of course

human language. And we have the ability

to work with every single region to

create their domain specific their

sovereign AI. Today we're announcing a

coalition to partner with us to make

Neotron 4 even more amazing. And that

coalition has some amazing companies in

it. Black Forest Labs imaging company.

Cursor the famous coding company we use

lots of it. Lang chain billion downloads

for creating custom agents. Mistrol the

Arthur Arthur mentioned I think he's

here. Incredible incredible company.

Perplexity Perplexes computer absolutely

use it everybody use it. It is so good.

A multimodal agentic system. Reflection

Sarv from India thinking machine mirror

Morardi's lab. Incredible companies

joining us. Thank you.

I said I said that every single

enterprise company, every single

software company in the world needs an

agentic systems, need an agent strategy.

you need to have an open claw strategy.

And they all agree

and they're all partnering with us to

integrate Nemo, the Nemo claw reference

design, the NVIDIA agentic AI toolkit,

and of course all of our open models.

One company after another. There's so

many. And we're partnering with all of

you. I'm really grateful for that. And

um this is our moment. This is a

reinvention. This is this is a

renaissance

a renaissance of the enterprise IT from

what would be a $2 trillion industry.

This is going to become a multi-

trillion dollar industry offering not

just tools for people to use but agents

that are specialized in very special

domains that you're expert in that we

could rent. I could totally imagine in

the future every single engineer in our

company will need an annual token

budget.

They're going to make a few hundred,000

a year their base pay. I'm going to give

them probably half of that on top of it

as tokens so that they could be

amplified 10x. Of course, we would. It

is now one of the recruiting tools in

Silicon Valley. how many tokens comes

along with my job. And the reason for

that is very clear because every

engineer that has access to tokens will

be more productive and those tokens as

you know will be produced by AI

factories that all of you and us we

partner to build. Okay. So every single

enterprise company in today sit on top

of file systems and data centers. Every

single software company of the future

will be agentic and they will be token

manufacturers. They'll be token users

for their engineers and they'll be token

manufacturers for all of their

customers. The open clause in event, the

open claw event cannot be understated.

This is as big of a deal as HTML. This

is as big of a deal as Linux. We have

now a world-class open agentic framework

that all of us could use to build our

open claw strategy. And we've created a

reference design we call Nemo cloud

neoclaw that all of you could use that

is optimized. It's performant. It is

safe and secure.

Speaking of agents, agents as you know

perceive, reason and act. Most of the

agents in the world today that I've

spoken about are digital agents. They

act in the digital world. They reason.

They write software. It's all digital.

But we also have been working on

physically embodied agents for a long

time. We call them robots. And the AIs

that they need are physical AIs. We have

some big announcements here. I'm going

to just walk through a few of them. 110

robots here. Almost every single company

in the world, I can't think of one that

are building robots is working with

Nvidia. We have three computers. The

training computer, the synthetic data

generation and simulation computer, and

of course the robotics computer that

sits inside the robot itself. We have

all the software stacks necessary to do

so. the AI models to help you.

And all of this is integrated into

ecosystems around the world and all of

our partners from Seammens to Cadence,

incredible partners everywhere. And

today, we're announcing a whole bunch of

new new partners. As you know, we've

been working on self-driving cars for a

long time. The Chad GPT moment of

self-driving cars has arrived. We now

know we could successfully autonomously

drive cars. And today we are announcing

four new partners for Nvidia's robo taxi

ready platform.

BYD,

Hyundai,

Nissan,

Ji all together, 18 million cars built

each year. joining our partners from

before Mercedes, Toyota,

GM. The number of robo taxi ready cars

in the future are going to be

incredible. And we're announcing also a

big partnership with Uber.

Multiple cities were going to be

deploying and connecting these robo taxi

ready vehicles into their network. And

so a whole bunch of new cars. We have uh

ABB, Universal Robotics, uh CUKA, so

many robotics companies here and we're

working with them to implement our

physical AI models integrated into

simulation system so that we could

deploy these robots into manufacturing

lines all over. We have Caterpillar

here. We even have T-Mobile here. And

the reason for that is in the future

that radio radio tower used to be a

radio tower is going to be an NVIDIA

aerial AI ram. And so this is going to

be a robotics radio tower. Meaning it

can reason about the traffic, figures

out how to adjust its beam forming so

that it could save as much energy as

possible and increase the amount of

fidelity as possible. There's so many

humanoid robots here, but one of my

favorites, one of my favorites is a

Disney robot. You know what? Tell you

what, let me just show you some of the

videos. Let's look at that first.

The first global rollout of physical AI

at scale is here. Autonomous vehicles.

And with NVIDIA Alpamo, vehicles now

have reasoning, helping them operate

safely and intelligently across

scenarios.

We ask the car to narrate its actions.

>> I'm changing lanes to the right to

follow my route.

>> Explain its thinking as it makes

decisions.

>> There's a double parked vehicle in my

lane. I'm going around it.

>> And follow instructions.

>> Hey, Mercedes. Can you speed up?

>> Sure, I'll speed up.

>> This is the age of physical AI and

robotics.

Around the world, developers are

building robots of every kind. But the

real world is massively diverse,

unpredictable, full of edge cases. Real

world data will never be enough to train

for every scenario.

We need data generated from AI and

simulation. For robots, compute is data.

Developers pre-trained World Foundation

models on internet scale video and human

demonstrations and evaluate the model's

performance to prepare them for

post-training.

Using classical and neural simulation,

they generate massive amounts of

synthetic data and train policies at

scale.

To accelerate developers, Nvidia built

open-source Isaac lab for robot training

and evaluation and simulation.

Newton for extensible and GPU

accelerated differentiable physics

simulation.

Cosmos world models for neural

simulation

and Groot open robotics foundation

models for robot reasoning and action

generation.

With enough compute, developers

everywhere are closing the physical AI

data gap.

Paratas AI trains their operating room

assistant robot in NVIDIA Isaac Lab,

multiplying their data with NVIDIA

Cosmos World models. Skilled AI uses

Isaac Lab and Cosmos to generate

post-training data for their skilled AI

brain. They use reinforcement learning

to harden the model across thousands of

variations. Humanoid

uses Isaac Lab to train whole body

control and manipulation policies.

Hexagon Robotics uses Isaac Lab for

training and data generation.

Foxcon fine-tunes group models in Isaac

Lab,

as does Noble Machines.

Disney research uses their chamino

physics simulator in Newton and Isaac

lab to train policies across their

character robots in every universe.

Da

Does

>> ladies and gentlemen Olaf

>> does coming through Newton Newton works.

>> Wow.

>> Omniverse works.

Olaf,

how are you?

>> I'm so happy now that I'm meeting you.

>> I know because I gave you your computer,

Jetson.

>> What's that?

Well, it's in your tummy.

>> That's going to be amazing.

>> And you learn how to walk inside

Omniverse.

>> I love walking. This is so much better

than riding on a reindeer gazing up at a

beautiful sky.

And

it was because of physics using this

Newton solver that runs on top of Nvidia

Warp that we jointly developed with

Disney and with DeepMind that made it

possible for you to be able to adapt to

the physical world. Check that out.

>> Not to say that

that's how smart you are.

>> I'm a snowman, not a snowed.

Could you imagine this? The future of

Disneyland. All these all these robots,

all these characters wandering around.

>> Oh,

>> you know, I have to admit though, I

thought you were going to be taller.

I've never seen such a short snowman, to

be honest.

>> Nope.

>> Hey, tell you what. You want to help me

out?

>> Hooray.

>> Okay. Usually usually I close the

keynote by talk telling you what I told

you. We talked about inference and

flection. We talked about the AI

factory. We talked about the open claw

agent revolution that's happening. And

of course we talked about physical AI

and robotics. But tell you what, why

don't we get some friends to help us

close it out?

>> Of course.

>> All right, play it.

Come on.

>> Terminating simulation.

Hello.

Anybody here?

The keynotes over all was said. Jensen

map the road ahead. AI factories coming

alive. Agents learning how to drive from

open models to robots too. Now we'll

break it all down.

Comput exploded. What we saw from CNN's

to open cloth agents working across the

land but they need the power to meet

demand. So we saw the problem it was

brilliant. We multiplied compute by 40

million.

But once upon AI time training was the

paradigm. Sure it talk models how in

France runs the whole world now shows us

who's the bars at 35 times less the cost

blackwell makes the token singing video

the inference king.

Yeah, our factories once took years as

vendors pulling racks and gears. Built

up slowly, piece by piece. No clear way

to scale this beast. DSX and Dynamo know

what to do.

Turning power

into revenue.

Agents used to wait and see, now act

autonomously. But if they ever try to

stray, safe claws block and say no way.

Nemo claws there to guard the course.

And yes, my friends,

it's open sorrow.

Cars that think and droids that run.

This ain't the movies. It's all begun.

Alamo calls the shots. It's a GPT moment

for the bots from sim streets. Now watch

them drive. Blow your hands up

for physical AI.

Industrial age. Build what came before.

Now we build for AI. Even more vera

rubin plus grog make the inference

splash put them together now it's

raining cash we build new architecture

every year cuz claws keep yelling more

tokens here the AI stacks for all to

make so let us all eat five layer cake

the moment's bright the path is clear

cuz open models led us here when data's

missing there's no dispute we just

generate more with compute robots

learning without flaw fueling the four

scaling laws the future's here won't you

come and see welcome Welcome all to GTC.

All right, have a great GTC

wave.

Thank you everybody.

I just met