All language subtitles for NVIDIA CEO Jensen Huang Leaves Everyone SPEECHLESS (Supercut).en

Now this particular system here is 40

Now this particular system here is 40
pedaflops which is approximately the

pedaflops which is approximately the

pedaflops which is approximately the
performance of

performance of

performance of
the Sierra supercomput in

the Sierra supercomput in

the Sierra supercomput in
2018. The Sierra supercomput has 18,000

2018. The Sierra supercomput has 18,000

2018. The Sierra supercomput has 18,000
GPUs volta GPUs. This one node here

GPUs volta GPUs. This one node here

GPUs volta GPUs. This one node here
replaces that entire supercomput.

4,000 times increase in performance in

4,000 times increase in performance in
six

six

six
years. That is extreme Moore's

years. That is extreme Moore's

years. That is extreme Moore's
law. Remember, I've said before that AI,

law. Remember, I've said before that AI,

law. Remember, I've said before that AI,
Nvidia has been scaling computing by

Nvidia has been scaling computing by

Nvidia has been scaling computing by
about a million times every 10 years,

about a million times every 10 years,

about a million times every 10 years,
and we're still on that track. And you

and we're still on that track. And you

and we're still on that track. And you
heard me say

heard me say

heard me say
before, the modern computer is an entire

before, the modern computer is an entire

before, the modern computer is an entire
data center. The data center is a unit

data center. The data center is a unit

data center. The data center is a unit
of computing. No longer just a PC, no

of computing. No longer just a PC, no

of computing. No longer just a PC, no
longer just a server. The entire data

longer just a server. The entire data

longer just a server. The entire data
center is running one job. And the

center is running one job. And the

center is running one job. And the
operating system would change. The

operating system would change. The

operating system would change. The
revolutionary computer called Hopper

revolutionary computer called Hopper

revolutionary computer called Hopper
came into the world about three years

came into the world about three years

came into the world about three years
ago and it revolutionized AI as we know

ago and it revolutionized AI as we know

ago and it revolutionized AI as we know
it. It became probably the most popular,

it. It became probably the most popular,

it. It became probably the most popular,
most well-known computer in the world.

most well-known computer in the world.

most well-known computer in the world.
In the last several years, we've been

In the last several years, we've been

In the last several years, we've been
working on a new computer to make it

working on a new computer to make it

working on a new computer to make it
possible for us to do inference time

possible for us to do inference time

possible for us to do inference time
scaling or basically thinking incredibly

scaling or basically thinking incredibly

scaling or basically thinking incredibly
fast because when you think you're

fast because when you think you're

fast because when you think you're
generating a lot of tokens in your head,

generating a lot of tokens in your head,

generating a lot of tokens in your head,
if you will, you're generating a lot of

if you will, you're generating a lot of

if you will, you're generating a lot of
thoughts and you iterate in your brain

thoughts and you iterate in your brain

thoughts and you iterate in your brain
before you produce the answer. So what

before you produce the answer. So what

before you produce the answer. So what
used to be oneshot AI is now going to be

used to be oneshot AI is now going to be

used to be oneshot AI is now going to be
thinking AI, reasoning AI, inference

thinking AI, reasoning AI, inference

thinking AI, reasoning AI, inference
time scaling AI and that's going to take

time scaling AI and that's going to take

time scaling AI and that's going to take
a lot more computation. And so we

a lot more computation. And so we

a lot more computation. And so we
created a new system called Grace

created a new system called Grace

created a new system called Grace
Blackwell. Grace Blackwell does several

Blackwell. Grace Blackwell does several

Blackwell. Grace Blackwell does several
things. It has the ability to scale up.

things. It has the ability to scale up.

things. It has the ability to scale up.
Scale up means to turn what is a

Scale up means to turn what is a

Scale up means to turn what is a
computer into a giant computer. Scale

computer into a giant computer. Scale

computer into a giant computer. Scale
out is to take a computer and connect

out is to take a computer and connect

out is to take a computer and connect
many of them together and let the work

many of them together and let the work

many of them together and let the work
be done in many different computers.

be done in many different computers.

be done in many different computers.
Scaling out is easy. Scaling up is

Scaling out is easy. Scaling up is

Scaling out is easy. Scaling up is
incredibly

incredibly

incredibly
hard. Building larger

hard. Building larger

hard. Building larger
computers that is beyond the limits of

computers that is beyond the limits of

computers that is beyond the limits of
semiconductor physics is insanely hard.

semiconductor physics is insanely hard.

semiconductor physics is insanely hard.
And that's what Grace Blackwell does.

And that's what Grace Blackwell does.

And that's what Grace Blackwell does.
It's available in coreweave now for

It's available in coreweave now for

It's available in coreweave now for
several weeks. It's already being used

several weeks. It's already being used

several weeks. It's already being used
by many CSPs. And now you're starting to

by many CSPs. And now you're starting to

by many CSPs. And now you're starting to
see it coming up from everywhere.

see it coming up from everywhere.

see it coming up from everywhere.
Everybody started to tweet out that

Everybody started to tweet out that

Everybody started to tweet out that
Grace Blackwell is in for production. In

Grace Blackwell is in for production. In

Grace Blackwell is in for production. In
Q3 of this year, just as I promised,

Q3 of this year, just as I promised,

Q3 of this year, just as I promised,
every single year, we will increase the

every single year, we will increase the

every single year, we will increase the
performance of our platform every single

performance of our platform every single

performance of our platform every single
year like Rhythm. And this this year in

year like Rhythm. And this this year in

year like Rhythm. And this this year in
Q3, we'll upgrade to Grace Blackwell

Q3, we'll upgrade to Grace Blackwell

Q3, we'll upgrade to Grace Blackwell
GB300. Same architecture, same physical

GB300. Same architecture, same physical

GB300. Same architecture, same physical
footprint, same electrical, mechanicals,

footprint, same electrical, mechanicals,

footprint, same electrical, mechanicals,
but the chips inside have been upgraded.

but the chips inside have been upgraded.

but the chips inside have been upgraded.
It has upgraded with a new black wall

It has upgraded with a new black wall

It has upgraded with a new black wall
chip is now one and a half times more

chip is now one and a half times more

chip is now one and a half times more
inference performance, has one and a

inference performance, has one and a

inference performance, has one and a
half times more HBM memory and it has

half times more HBM memory and it has

half times more HBM memory and it has
two times more networking. And so the

two times more networking. And so the

two times more networking. And so the
overall system performance is higher.

overall system performance is higher.

overall system performance is higher.
Well, let's take a look at what's inside

Well, let's take a look at what's inside

Well, let's take a look at what's inside
Grace Blackwell. Grace Blackwell starts

Grace Blackwell. Grace Blackwell starts

Grace Blackwell. Grace Blackwell starts
with this compute node. This compute

with this compute node. This compute

with this compute node. This compute
node right here, this is one of the

node right here, this is one of the

node right here, this is one of the
compute nodes. This is um what the last

compute nodes. This is um what the last

compute nodes. This is um what the last
generation looks like. the B200. This is

generation looks like. the B200. This is

generation looks like. the B200. This is
what B300 looks like. Notice right here

what B300 looks like. Notice right here

what B300 looks like. Notice right here
in the center, it's 100% liquid cooled

in the center, it's 100% liquid cooled

in the center, it's 100% liquid cooled
now, but otherwise externally it's the

now, but otherwise externally it's the

now, but otherwise externally it's the
same. You could plug it into the same

same. You could plug it into the same

same. You could plug it into the same
systems and same chassis. And so this is

systems and same chassis. And so this is

systems and same chassis. And so this is
the Grace Blackwell GB300 system. It's

the Grace Blackwell GB300 system. It's

the Grace Blackwell GB300 system. It's
one and a half times more inference

one and a half times more inference

one and a half times more inference
performance. The training performance is

performance. The training performance is

performance. The training performance is
about the same, but the inference

about the same, but the inference

about the same, but the inference
performance is one and a half times

performance is one and a half times

performance is one and a half times
more. Remember, I've said before that

more. Remember, I've said before that

more. Remember, I've said before that
AI, Nvidia has been scaling computing by

AI, Nvidia has been scaling computing by

AI, Nvidia has been scaling computing by
about a million times every 10 years,

about a million times every 10 years,

about a million times every 10 years,
and we're still on that track. But the

and we're still on that track. But the

and we're still on that track. But the
way to do that is not just to make the

way to do that is not just to make the

way to do that is not just to make the
chips faster. There's only a limit to

chips faster. There's only a limit to

chips faster. There's only a limit to
how fast you can make chips and how big

how fast you can make chips and how big

how fast you can make chips and how big
you can make chips. In the case of

you can make chips. In the case of

you can make chips. In the case of
Blackwell, we even connected two chips

Blackwell, we even connected two chips

Blackwell, we even connected two chips
together to make it possible. TSMC

together to make it possible. TSMC

together to make it possible. TSMC
worked with us to invent a new co-as

worked with us to invent a new co-as

worked with us to invent a new co-as
process called cos l that made it

process called cos l that made it

process called cos l that made it
possible for us to create these giant

possible for us to create these giant

possible for us to create these giant
chips but still we want chips way bigger

chips but still we want chips way bigger

chips but still we want chips way bigger
than that and so we had to create what

than that and so we had to create what

than that and so we had to create what
is called mvlink this is the world's

is called mvlink this is the world's

is called mvlink this is the world's
fastest switch this MVL link here right

fastest switch this MVL link here right

fastest switch this MVL link here right
here is 7.2 2 terabytes per second. Nine

here is 7.2 2 terabytes per second. Nine

here is 7.2 2 terabytes per second. Nine
of these go into that rack. And that

of these go into that rack. And that

of these go into that rack. And that
nine, those nine switches are connected

nine, those nine switches are connected

nine, those nine switches are connected
by this miracle. This is um quite

by this miracle. This is um quite

by this miracle. This is um quite
heavy. That's because I'm quite strong.

heavy. That's because I'm quite strong.

heavy. That's because I'm quite strong.
I made it I made it look so light, but

I made it I made it look so light, but

I made it I made it look so light, but
this is almost this is 70

this is almost this is 70

this is almost this is 70
lbs. And so this is the MVLength spine.

lbs. And so this is the MVLength spine.

lbs. And so this is the MVLength spine.
Two miles of

Two miles of

Two miles of
cables, 5,000 cables

cables, 5,000 cables

cables, 5,000 cables
structured, all

structured, all

structured, all
coaxed impen matched and it connects all

coaxed impen matched and it connects all

coaxed impen matched and it connects all
72 GPUs to all of the other 72 GPUs

72 GPUs to all of the other 72 GPUs

72 GPUs to all of the other 72 GPUs
across this network called MVLink

across this network called MVLink

across this network called MVLink
switch. 130 terabytes per second of

switch. 130 terabytes per second of

switch. 130 terabytes per second of
bandwidth across the MVLink spine. So

bandwidth across the MVLink spine. So

bandwidth across the MVLink spine. So
just put in

just put in

just put in
perspective the

perspective the

perspective the
peak traffic of the entire

peak traffic of the entire

peak traffic of the entire
internet the peak traffic of the entire

internet the peak traffic of the entire

internet the peak traffic of the entire
internet is

internet is

internet is
900

900

900
terabits per

terabits per

terabits per
second. Divide that by

second. Divide that by

second. Divide that by
eight.

eight.

eight.
This moves more traffic than the entire

internet. one MVLink spine across this

internet. one MVLink spine across this
MV nine of these MVLink switches so that

MV nine of these MVLink switches so that

MV nine of these MVLink switches so that
every single GPU can talk to every other

every single GPU can talk to every other

every single GPU can talk to every other
GPU at exactly the same time. This is

GPU at exactly the same time. This is

GPU at exactly the same time. This is
the miracle

of

of
GB200 and because there's a limit to how

GB200 and because there's a limit to how

GB200 and because there's a limit to how
far you can drive Certis. This is as far

far you can drive Certis. This is as far

far you can drive Certis. This is as far
as any Certis has ever driven from CH.

as any Certis has ever driven from CH.

as any Certis has ever driven from CH.
This goes chip to the switch out to the

This goes chip to the switch out to the

This goes chip to the switch out to the
spine to any other any other any other

spine to any other any other any other

spine to any other any other any other
switch any other chip all electrical.

switch any other chip all electrical.

switch any other chip all electrical.
And so that limit caused us to put

And so that limit caused us to put

And so that limit caused us to put
everything in one rack. That one rack is

everything in one rack. That one rack is

everything in one rack. That one rack is
120 kilowatts which is the reason why

120 kilowatts which is the reason why

120 kilowatts which is the reason why
everything has to be liquid cooled. We

everything has to be liquid cooled. We

everything has to be liquid cooled. We
now have the ability to disagregate the

now have the ability to disagregate the

now have the ability to disagregate the
GPUs out of one motherboard essentially

GPUs out of one motherboard essentially

GPUs out of one motherboard essentially
across an entire rack. And so that

across an entire rack. And so that

across an entire rack. And so that
entire rack is one motherboard. That's

entire rack is one motherboard. That's

entire rack is one motherboard. That's
the miracle completely disagregated. And

the miracle completely disagregated. And

the miracle completely disagregated. And
now the GPU performance is incredible.

now the GPU performance is incredible.

now the GPU performance is incredible.
The amount of memory is incredible. The

The amount of memory is incredible. The

The amount of memory is incredible. The
networking bandwidth is incredible. And

networking bandwidth is incredible. And

networking bandwidth is incredible. And
now we can really scale these out. Once

now we can really scale these out. Once

now we can really scale these out. Once
we scale it up, then we can scale it out

we scale it up, then we can scale it out

we scale it up, then we can scale it out
into large systems. And notice almost

into large systems. And notice almost

into large systems. And notice almost
everything Nvidia builds are gigantic.

everything Nvidia builds are gigantic.

everything Nvidia builds are gigantic.
And the reason for that is because we're

And the reason for that is because we're

And the reason for that is because we're
not building data centers and servers.

not building data centers and servers.

not building data centers and servers.
We're building AI factories. This is

We're building AI factories. This is

We're building AI factories. This is
Coreweave. This is Oracle Cloud. And

Coreweave. This is Oracle Cloud. And

Coreweave. This is Oracle Cloud. And
this is the XAI Colossus factory. This

this is the XAI Colossus factory. This

this is the XAI Colossus factory. This
is

is

is
Stargate. 4 million square feet. 4

Stargate. 4 million square feet. 4

Stargate. 4 million square feet. 4
million square feet. One gigawatt. And

million square feet. One gigawatt. And

million square feet. One gigawatt. And
so just think about this factory here.

so just think about this factory here.

so just think about this factory here.
This one gawatt factory. This one gawatt

This one gawatt factory. This one gawatt

This one gawatt factory. This one gawatt
factory is probably going to be about,

factory is probably going to be about,

factory is probably going to be about,
you know, 60 to80 billion. out of that

you know, 60 to80 billion. out of that

you know, 60 to80 billion. out of that
60 to80 billion the electronics the

60 to80 billion the electronics the

60 to80 billion the electronics the
computing part of it these systems are

computing part of it these systems are

computing part of it these systems are
4050 billion dollars of it and so these

4050 billion dollars of it and so these

4050 billion dollars of it and so these
are gigantic factory

are gigantic factory

are gigantic factory
investments the reason why people build

investments the reason why people build

investments the reason why people build
factories is

factories is

factories is
because you know you know the answer the

because you know you know the answer the

because you know you know the answer the
more you the more you

buy say it with me the more you buy the

buy say it with me the more you buy the
more you

more you

more you
make. That's what factories

make. That's what factories

make. That's what factories
do.

do.

do.
Okay. And so today we're announcing

Okay. And so today we're announcing

Okay. And so today we're announcing
something very

something very

something very
special. We're announcing NVIDIA MVLink

special. We're announcing NVIDIA MVLink

special. We're announcing NVIDIA MVLink
Fusion.

Fusion.

Fusion.
Envy Link

Envy Link

Envy Link
Fusion is so that you can

Fusion is so that you can

Fusion is so that you can
build

build

build
semicustom AI

semicustom AI

semicustom AI
infrastructure, not just semi-custom

infrastructure, not just semi-custom

infrastructure, not just semi-custom
chips, because those are the good old

chips, because those are the good old

chips, because those are the good old
days. You want to build AI

days. You want to build AI

days. You want to build AI
infrastructure. And everybody's AI

infrastructure. And everybody's AI

infrastructure. And everybody's AI
infrastructure could be a little

infrastructure could be a little

infrastructure could be a little
different. Some of you could have a lot

different. Some of you could have a lot

different. Some of you could have a lot
more CPUs and some of it could have a

more CPUs and some of it could have a

more CPUs and some of it could have a
lot more Nvidia GPUs and some of it

lot more Nvidia GPUs and some of it

lot more Nvidia GPUs and some of it
could be somebody's semi-custom AS6. And

could be somebody's semi-custom AS6. And

could be somebody's semi-custom AS6. And
those systems are so insanely hard to

those systems are so insanely hard to

those systems are so insanely hard to
build. And they're all missing this one

build. And they're all missing this one

build. And they're all missing this one
incredible ingredient. This incredible

incredible ingredient. This incredible

incredible ingredient. This incredible
ingredient called MVLink. MVLink so that

ingredient called MVLink. MVLink so that

ingredient called MVLink. MVLink so that
you could scale up these semi-custom

you could scale up these semi-custom

you could scale up these semi-custom
systems and build really powerful

systems and build really powerful

systems and build really powerful
computers. And so today we're announcing

computers. And so today we're announcing

computers. And so today we're announcing
the MVLink Fusion. MVLink Fusion kind of

the MVLink Fusion. MVLink Fusion kind of

the MVLink Fusion. MVLink Fusion kind of
works like this. This is the Nvidia

works like this. This is the Nvidia

works like this. This is the Nvidia
platform, 100% Nvidia. You got Nvidia

platform, 100% Nvidia. You got Nvidia

platform, 100% Nvidia. You got Nvidia
CPU, Nvidia GPU, the MVLink

CPU, Nvidia GPU, the MVLink

CPU, Nvidia GPU, the MVLink
switches, the networking from Nvidia

switches, the networking from Nvidia

switches, the networking from Nvidia
called Spectrum X or

called Spectrum X or

called Spectrum X or
Infiniband,

Infiniband,

Infiniband,
Nyx, network

Nyx, network

Nyx, network
interconnects,

interconnects,

interconnects,
switches and all of the entire system,

switches and all of the entire system,

switches and all of the entire system,
the entire infrastructure built end to

the entire infrastructure built end to

the entire infrastructure built end to
end. And we now today make it possible

end. And we now today make it possible

end. And we now today make it possible
for you to mix and match it even at the

for you to mix and match it even at the

for you to mix and match it even at the
compute level. This would be what you

compute level. This would be what you

compute level. This would be what you
would do using your custom ASIC. And it

would do using your custom ASIC. And it

would do using your custom ASIC. And it
doesn't have to be just a transformer

doesn't have to be just a transformer

doesn't have to be just a transformer
accelerator. It could be an accelerator

accelerator. It could be an accelerator

accelerator. It could be an accelerator
of any kind that you would like to

of any kind that you would like to

of any kind that you would like to
integrate into a large scaleup

integrate into a large scaleup

integrate into a large scaleup
system. We create an MVLink chiplet.

system. We create an MVLink chiplet.

system. We create an MVLink chiplet.
It's basically a switch that a butts

It's basically a switch that a butts

It's basically a switch that a butts
right up to your chip.

right up to your chip.

right up to your chip.
There's IP that will be available to

There's IP that will be available to

There's IP that will be available to
integrate into your semi-custom ASIC.

integrate into your semi-custom ASIC.

integrate into your semi-custom ASIC.
You've been building your own CPU for

You've been building your own CPU for

You've been building your own CPU for
some time and maybe your CPU has built a

some time and maybe your CPU has built a

some time and maybe your CPU has built a
very large ecosystem and you would like

very large ecosystem and you would like

very large ecosystem and you would like
to integrate Nvidia into your

to integrate Nvidia into your

to integrate Nvidia into your
ecosystem and now we can make it

ecosystem and now we can make it

ecosystem and now we can make it
possible for you to do that. You could

possible for you to do that. You could

possible for you to do that. You could
do that by building a your custom CPU.

do that by building a your custom CPU.

do that by building a your custom CPU.
We provide you with our MVLink chip to

We provide you with our MVLink chip to

We provide you with our MVLink chip to
chip interface into your ASIC. We

chip interface into your ASIC. We

chip interface into your ASIC. We
connect it with MVLink chiplets and now

connect it with MVLink chiplets and now

connect it with MVLink chiplets and now
it connects and directly abuts

it connects and directly abuts

it connects and directly abuts
into the Blackwell chips and our next

into the Blackwell chips and our next

into the Blackwell chips and our next
generation Reuben chips. And again, it

generation Reuben chips. And again, it

generation Reuben chips. And again, it
fits right into this ecosystem. This

fits right into this ecosystem. This

fits right into this ecosystem. This
incredible body of work now becomes

incredible body of work now becomes

incredible body of work now becomes
flexible and open for everybody to

flexible and open for everybody to

flexible and open for everybody to
integrate into. you instantly get

integrate into. you instantly get

integrate into. you instantly get
integrated into the entire larger Nvidia

integrated into the entire larger Nvidia

integrated into the entire larger Nvidia
ecosystem that makes it possible for you

ecosystem that makes it possible for you

ecosystem that makes it possible for you
to scale up into these AI

to scale up into these AI

to scale up into these AI
supercomputers. Now, let me talk to you

supercomputers. Now, let me talk to you

supercomputers. Now, let me talk to you
about some new product categories. As

about some new product categories. As

about some new product categories. As
you know, I've shown you a couple of

you know, I've shown you a couple of

you know, I've shown you a couple of
different

different

different
computers.

computers.

computers.
However, in order to serve the vast

However, in order to serve the vast

However, in order to serve the vast
majority of the world, there are still

majority of the world, there are still

majority of the world, there are still
some computers that are missing. And so,

some computers that are missing. And so,

some computers that are missing. And so,
I'm going to talk about them. This new

I'm going to talk about them. This new

I'm going to talk about them. This new
computer we call DGX Spark is in full

computer we call DGX Spark is in full

computer we call DGX Spark is in full
production. DJX

Spark will be ready, will be available

Spark will be ready, will be available
shortly, probably in a few weeks. We

shortly, probably in a few weeks. We

shortly, probably in a few weeks. We
have tremendous partners working with

have tremendous partners working with

have tremendous partners working with
us. Dell,

us. Dell,

us. Dell,
HPI, Asus, MSI,

HPI, Asus, MSI,

HPI, Asus, MSI,
Gigabyte,

Gigabyte,

Gigabyte,
Lenovo, incredible partners with working

Lenovo, incredible partners with working

Lenovo, incredible partners with working
with us. And this is the DJX Spark. This

with us. And this is the DJX Spark. This

with us. And this is the DJX Spark. This
is actually a production unit. This is

is actually a production unit. This is

is actually a production unit. This is
our version. This is our version.

our version. This is our version.

our version. This is our version.
However, our partners are building a

However, our partners are building a

However, our partners are building a
whole bunch of different

whole bunch of different

whole bunch of different
versions. This is designed for AI native

versions. This is designed for AI native

versions. This is designed for AI native
developers. If you're a developer,

developers. If you're a developer,

developers. If you're a developer,
you're a student, you're a researcher,

you're a student, you're a researcher,

you're a student, you're a researcher,
and you don't want to keep opening up

and you don't want to keep opening up

and you don't want to keep opening up
the cloud and getting it prepared and

the cloud and getting it prepared and

the cloud and getting it prepared and
then when you're done scrubbing it,

then when you're done scrubbing it,

then when you're done scrubbing it,
okay, but you would just like to have

okay, but you would just like to have

okay, but you would just like to have
your own basically your own AI cloud

your own basically your own AI cloud

your own basically your own AI cloud
sitting right next to you and it's

sitting right next to you and it's

sitting right next to you and it's
always on, always waiting for you. It

always on, always waiting for you. It

always on, always waiting for you. It
allows you to do your prototyping early

allows you to do your prototyping early

allows you to do your prototyping early
development. And this is what's amazing.

development. And this is what's amazing.

development. And this is what's amazing.
This is um DGX Spark. It's one

This is um DGX Spark. It's one

This is um DGX Spark. It's one
pedlops and 128

pedlops and 128

pedlops and 128
gigabytes. In

gigabytes. In

gigabytes. In
2016, when I delivered DGX1, this is

2016, when I delivered DGX1, this is

2016, when I delivered DGX1, this is
just the bezel. I can't lift a whole

just the bezel. I can't lift a whole

just the bezel. I can't lift a whole
computer. It's 300 lb. This is

computer. It's 300 lb. This is

computer. It's 300 lb. This is
DGX1. This is one

DGX1. This is one

DGX1. This is one
pedlops and 128 gigabytes. Of course,

pedlops and 128 gigabytes. Of course,

pedlops and 128 gigabytes. Of course,
this is 128 gigabytes of HBM memory and

this is 128 gigabytes of HBM memory and

this is 128 gigabytes of HBM memory and
this is 128 GB of

this is 128 GB of

this is 128 GB of
LPDDR5X. The performance is in fact

LPDDR5X. The performance is in fact

LPDDR5X. The performance is in fact
quite similar. But what's most important

quite similar. But what's most important

quite similar. But what's most important
is that the work that you could do you

is that the work that you could do you

is that the work that you could do you
could work on this is the same work you

could work on this is the same work you

could work on this is the same work you
could do here. It's an incredible

could do here. It's an incredible

could do here. It's an incredible
achievement over just the course of

achievement over just the course of

achievement over just the course of
about 10 years. Okay, so this is DGX

about 10 years. Okay, so this is DGX

about 10 years. Okay, so this is DGX
Spark for anybody who would like to have

Spark for anybody who would like to have

Spark for anybody who would like to have
their

their

their
own AI supercomputer. If that one isn't

own AI supercomputer. If that one isn't

own AI supercomputer. If that one isn't
big enough for you, here's one. This is

big enough for you, here's one. This is

big enough for you, here's one. This is
another desk side. This is also going to

another desk side. This is also going to

another desk side. This is also going to
be available from Dell and HPI, Asus,

be available from Dell and HPI, Asus,

be available from Dell and HPI, Asus,
Gigabyte, MSI, Lenovo, amazing

Gigabyte, MSI, Lenovo, amazing

Gigabyte, MSI, Lenovo, amazing
workstation companies. And this is going

workstation companies. And this is going

workstation companies. And this is going
to be your own personal

to be your own personal

to be your own personal
DGX

DGX

DGX
supercomput. This computer is the most

supercomput. This computer is the most

supercomput. This computer is the most
performance you can possibly get out of

performance you can possibly get out of

performance you can possibly get out of
a wall socket. You could put this in

a wall socket. You could put this in

a wall socket. You could put this in
your

your

your
kitchen, but just

kitchen, but just

kitchen, but just
barely. If you put this in your kitchen

barely. If you put this in your kitchen

barely. If you put this in your kitchen
and then somebody runs the microwave, I

and then somebody runs the microwave, I

and then somebody runs the microwave, I
think that's the limit. And so this is

think that's the limit. And so this is

think that's the limit. And so this is
the limit. This is the limit of what you

the limit. This is the limit of what you

the limit. This is the limit of what you
can get out of a wall outlet. And this

can get out of a wall outlet. And this

can get out of a wall outlet. And this
is a DGX station. The programming model

is a DGX station. The programming model

is a DGX station. The programming model
of this and the giant systems that I

of this and the giant systems that I

of this and the giant systems that I
showed you are the same. That's the

showed you are the same. That's the

showed you are the same. That's the
amazing thing. one architecture, one

amazing thing. one architecture, one

amazing thing. one architecture, one
architecture. And this has the ability,

architecture. And this has the ability,

architecture. And this has the ability,
enough capacity and performance to run a

enough capacity and performance to run a

enough capacity and performance to run a
one trillion parameter AI model.

one trillion parameter AI model.

one trillion parameter AI model.
Remember Llama is Llama 70B. A one

Remember Llama is Llama 70B. A one

Remember Llama is Llama 70B. A one
trillion parameter model is going to run

trillion parameter model is going to run

trillion parameter model is going to run
wonderfully on this machine. Okay, so

wonderfully on this machine. Okay, so

wonderfully on this machine. Okay, so
that's the DGX station. But in order for

that's the DGX station. But in order for

that's the DGX station. But in order for
us to bring AI into a new world and this

us to bring AI into a new world and this

us to bring AI into a new world and this
new world is enterprise IT, we have to

new world is enterprise IT, we have to

new world is enterprise IT, we have to
go back to our roots and we have to

go back to our roots and we have to

go back to our roots and we have to
reinvent computing and bring AI into

reinvent computing and bring AI into

reinvent computing and bring AI into
traditional enterprise computing. And so

traditional enterprise computing. And so

traditional enterprise computing. And so
let's take a look at that.

This is the brand new RTX Pro. RTX Pro

This is the brand new RTX Pro. RTX Pro
Enterprise and Omniverse server. This

Enterprise and Omniverse server. This

Enterprise and Omniverse server. This
server can run everything. Everything

server can run everything. Everything

server can run everything. Everything
that runs in the world today should run

that runs in the world today should run

that runs in the world today should run
here. Omniverse runs on here perfectly.

here. Omniverse runs on here perfectly.

here. Omniverse runs on here perfectly.
But in addition to that, in addition to

But in addition to that, in addition to

But in addition to that, in addition to
that, this is the

that, this is the

that, this is the
computer for enterprise AI

computer for enterprise AI

computer for enterprise AI
agents. Those AI agents could be only

agents. Those AI agents could be only

agents. Those AI agents could be only
text. Those AI agents could also be

text. Those AI agents could also be

text. Those AI agents could also be
computer graphics, could be in video

computer graphics, could be in video

computer graphics, could be in video
form. All of those workloads work on

form. All of those workloads work on

form. All of those workloads work on
this system. No matter the modality,

this system. No matter the modality,

this system. No matter the modality,
every single model that we know of in

every single model that we know of in

every single model that we know of in
the world, every application that we

the world, every application that we

the world, every application that we
know of should run on this. In fact,

know of should run on this. In fact,

know of should run on this. In fact,
even Crisis works on here. Okay? So,

even Crisis works on here. Okay? So,

even Crisis works on here. Okay? So,
anybody who's a GeForce

anybody who's a GeForce

anybody who's a GeForce
gamer, there are no GeForce gamer in the

gamer, there are no GeForce gamer in the

gamer, there are no GeForce gamer in the
room. What connects these eight GPUs,

room. What connects these eight GPUs,

room. What connects these eight GPUs,
the Blackwell, new Blackwell RTX, RTX

the Blackwell, new Blackwell RTX, RTX

the Blackwell, new Blackwell RTX, RTX
Pro 6000s, is this new motherboard. This

Pro 6000s, is this new motherboard. This

Pro 6000s, is this new motherboard. This
new motherboard is actually a switched

new motherboard is actually a switched

new motherboard is actually a switched
network. CX8 is a new category of chips.

network. CX8 is a new category of chips.

network. CX8 is a new category of chips.
It's a switch first, networking chip

It's a switch first, networking chip

It's a switch first, networking chip
second. It's also the most advanced

second. It's also the most advanced

second. It's also the most advanced
networking chip in the world. So each

networking chip in the world. So each

networking chip in the world. So each
one of these GPUs have their own

one of these GPUs have their own

one of these GPUs have their own
networking interface. All of the GPUs

networking interface. All of the GPUs

networking interface. All of the GPUs
are now communicating to all of the

are now communicating to all of the

are now communicating to all of the
other GPUs on east west traffic.

other GPUs on east west traffic.

other GPUs on east west traffic.
Incredible performance. Okay, let's talk

Incredible performance. Okay, let's talk

Incredible performance. Okay, let's talk
about robots.

about robots.

about robots.
So agent AIs, agentic AIs, AI agents, a

So agent AIs, agentic AIs, AI agents, a

So agent AIs, agentic AIs, AI agents, a
lot of different ways to say it. Agents

lot of different ways to say it. Agents

lot of different ways to say it. Agents
are essentially digital robots. Reason

are essentially digital robots. Reason

are essentially digital robots. Reason
for that is because a robot perceives,

for that is because a robot perceives,

for that is because a robot perceives,
understands, and plans. And that's

understands, and plans. And that's

understands, and plans. And that's
essentially what agents do. But we would

essentially what agents do. But we would

essentially what agents do. But we would
like to build also physical robots. And

like to build also physical robots. And

like to build also physical robots. And
these physical robots first it starts

these physical robots first it starts

these physical robots first it starts
with the ability to learn to be a robot.

with the ability to learn to be a robot.

with the ability to learn to be a robot.
Now you know that we've been working in

Now you know that we've been working in

Now you know that we've been working in
in autonomous systems for some time. Our

in autonomous systems for some time. Our

in autonomous systems for some time. Our
self-driving car basically has three

self-driving car basically has three

self-driving car basically has three
systems. There's the system for creating

systems. There's the system for creating

systems. There's the system for creating
the AI model and that's GB200, GB300.

the AI model and that's GB200, GB300.

the AI model and that's GB200, GB300.
It's going to be used for that, training

It's going to be used for that, training

It's going to be used for that, training
the AI model. Then you have Omniverse

the AI model. Then you have Omniverse

the AI model. Then you have Omniverse
for simulating the AI model. And then

for simulating the AI model. And then

for simulating the AI model. And then
when you're done with that AI model, you

when you're done with that AI model, you

when you're done with that AI model, you
put that model, the AI into the

put that model, the AI into the

put that model, the AI into the
self-driving car. So we're doing exactly

self-driving car. So we're doing exactly

self-driving car. So we're doing exactly
the same thing in robotic systems just

the same thing in robotic systems just

the same thing in robotic systems just
like cars. And so this is our Isaac

like cars. And so this is our Isaac

like cars. And so this is our Isaac
Groot platform. The simulation is

Groot platform. The simulation is

Groot platform. The simulation is
exactly the same. It's omniverse. The

exactly the same. It's omniverse. The

exactly the same. It's omniverse. The
compute, the training system is the

compute, the training system is the

compute, the training system is the
same. When you're done with the model,

same. When you're done with the model,

same. When you're done with the model,
you put it into inside this Isaac group

you put it into inside this Isaac group

you put it into inside this Isaac group
platform. It Isaac Group platform starts

platform. It Isaac Group platform starts

platform. It Isaac Group platform starts
with a brand new computer called Jetson

with a brand new computer called Jetson

with a brand new computer called Jetson
Thor. This is just started in

Thor. This is just started in

Thor. This is just started in
production. It is an incredible

production. It is an incredible

production. It is an incredible
processor. Basically, a robotic

processor. Basically, a robotic

processor. Basically, a robotic
processor goes to self-driving cars and

processor goes to self-driving cars and

processor goes to self-driving cars and
it goes into a human or robotic system.

it goes into a human or robotic system.

it goes into a human or robotic system.
On top is an operating system we called

On top is an operating system we called

On top is an operating system we called
Isaac Nvidia Isaac. The Nvidia Isaac

Isaac Nvidia Isaac. The Nvidia Isaac

Isaac Nvidia Isaac. The Nvidia Isaac
operating system is the runtime. It does

operating system is the runtime. It does

operating system is the runtime. It does
all of the neuronet network processing,

all of the neuronet network processing,

all of the neuronet network processing,
the sensor processing, pipelines all of

the sensor processing, pipelines all of

the sensor processing, pipelines all of
it and deliver actuated results. The

it and deliver actuated results. The

it and deliver actuated results. The
biggest challenge in robotics is and

biggest challenge in robotics is and

biggest challenge in robotics is and
well the biggest challenge in AI overall

well the biggest challenge in AI overall

well the biggest challenge in AI overall
is what is your data

is what is your data

is what is your data
strategy and your data strategy has to

strategy and your data strategy has to

strategy and your data strategy has to
be that's where great deal of research

be that's where great deal of research

be that's where great deal of research
and a great deal of technology goes into

and a great deal of technology goes into

and a great deal of technology goes into
in the case of robotics human

in the case of robotics human

in the case of robotics human
demonstration just like we demonstrate

demonstration just like we demonstrate

demonstration just like we demonstrate
to our children or a a coach

to our children or a a coach

to our children or a a coach
demonstrates to an athlete you

demonstrates to an athlete you

demonstrates to an athlete you
demonstrate using

demonstrate using

demonstrate using
teleoperations you demonstrate to the

teleoperations you demonstrate to the

teleoperations you demonstrate to the
robot how to perform the task and the

robot how to perform the task and the

robot how to perform the task and the
robot can generalize ize from that

robot can generalize ize from that

robot can generalize ize from that
demonstration because AI can generalize

demonstration because AI can generalize

demonstration because AI can generalize
and we have technology for

and we have technology for

and we have technology for
generalization. You can generalize from

generalization. You can generalize from

generalization. You can generalize from
that one demonstration other techniques.

that one demonstration other techniques.

that one demonstration other techniques.
Okay. And so what

Okay. And so what

Okay. And so what
if what if you want to teach this robot

if what if you want to teach this robot

if what if you want to teach this robot
a whole bunch of skills? How many

a whole bunch of skills? How many

a whole bunch of skills? How many
different teleoperation people do you

different teleoperation people do you

different teleoperation people do you
need? Well, it turns out to be a lot.

need? Well, it turns out to be a lot.

need? Well, it turns out to be a lot.
And so what we decided to do was use AI

And so what we decided to do was use AI

And so what we decided to do was use AI
to

to

to
amplify the human demonstration systems.

amplify the human demonstration systems.

amplify the human demonstration systems.
And so this is essentially going from

And so this is essentially going from

And so this is essentially going from
real to real and using an AI to help

real to real and using an AI to help

real to real and using an AI to help
us expand amplify the amount of data

us expand amplify the amount of data

us expand amplify the amount of data
that was collected during human

that was collected during human

that was collected during human
demonstration to train an AI

demonstration to train an AI

demonstration to train an AI
model. Is that correct?

model. Is that correct?

model. Is that correct?
So in order for robotics to happen, you

So in order for robotics to happen, you

So in order for robotics to happen, you
need you need AI. But in order to teach

need you need AI. But in order to teach

need you need AI. But in order to teach
the AI, you need AI. And so this is

the AI, you need AI. And so this is

the AI, you need AI. And so this is
really the great thing

really the great thing

really the great thing
about the era of agents where we need a

about the era of agents where we need a

about the era of agents where we need a
a large amount of synthetic data

a large amount of synthetic data

a large amount of synthetic data
generation, robotics, a large amount of

generation, robotics, a large amount of

generation, robotics, a large amount of
synthetic data generation and skill

synthetic data generation and skill

synthetic data generation and skill
learning called fine-tuning, which is a

learning called fine-tuning, which is a

learning called fine-tuning, which is a
lot of reinforcement learning and

lot of reinforcement learning and

lot of reinforcement learning and
enormous amount of compute. And so this

enormous amount of compute. And so this

enormous amount of compute. And so this
is an er this is a whole era where the

is an er this is a whole era where the

is an er this is a whole era where the
training of these AI the development of

training of these AI the development of

training of these AI the development of
these AI as well as the running of the

these AI as well as the running of the

these AI as well as the running of the
AI needs an enormous amount of compute.

AI needs an enormous amount of compute.

AI needs an enormous amount of compute.
Well, as I mentioned earlier, the world

Well, as I mentioned earlier, the world

Well, as I mentioned earlier, the world
has a severe shortage of labor. And the

has a severe shortage of labor. And the

has a severe shortage of labor. And the
reason why humano robotics is so

reason why humano robotics is so

reason why humano robotics is so
important is because it is the only form

important is because it is the only form

important is because it is the only form
of robot that can be deployed almost

of robot that can be deployed almost

of robot that can be deployed almost
anywhere, brownfield. It doesn't have to

anywhere, brownfield. It doesn't have to

anywhere, brownfield. It doesn't have to
be green field. It could fit into the

be green field. It could fit into the

be green field. It could fit into the
world we created. It could do the task

world we created. It could do the task

world we created. It could do the task
that we made for ourselves. We

that we made for ourselves. We

that we made for ourselves. We
engineered the world for ourselves and

engineered the world for ourselves and

engineered the world for ourselves and
now we could create a robot that fit

now we could create a robot that fit

now we could create a robot that fit
into that world to help us. Now the

into that world to help us. Now the

into that world to help us. Now the
amazing thing about human robotics is

amazing thing about human robotics is

amazing thing about human robotics is
not just the fact that if it worked it

not just the fact that if it worked it

not just the fact that if it worked it
could be quite versatile. It is likely

could be quite versatile. It is likely

could be quite versatile. It is likely
the only robot that is likely to work

the only robot that is likely to work

the only robot that is likely to work
and the reason for that is because

and the reason for that is because

and the reason for that is because
technology needs scale.

technology needs scale.

technology needs scale.
Most of the robotic systems we've had so

Most of the robotic systems we've had so

Most of the robotic systems we've had so
far are too low volume. And those low

far are too low volume. And those low

far are too low volume. And those low
volume systems will never achieve the

volume systems will never achieve the

volume systems will never achieve the
technology scale to get the flywheel

technology scale to get the flywheel

technology scale to get the flywheel
going far enough fast enough so that

going far enough fast enough so that

going far enough fast enough so that
we're willing to dedicate enough

we're willing to dedicate enough

we're willing to dedicate enough
technology into it to make it better.

technology into it to make it better.

technology into it to make it better.
But human or robot, it is likely to be

But human or robot, it is likely to be

But human or robot, it is likely to be
the next multi-trillion dollar industry.

the next multi-trillion dollar industry.

the next multi-trillion dollar industry.
And the technology innovation is

And the technology innovation is

And the technology innovation is
incredibly fast and the consumption of

incredibly fast and the consumption of

incredibly fast and the consumption of
computing and data centers enormous. But

computing and data centers enormous. But

computing and data centers enormous. But
this is one of those applications that

this is one of those applications that

this is one of those applications that
needs three computers. One computer is

needs three computers. One computer is

needs three computers. One computer is
an AI for learning. One computer is a

an AI for learning. One computer is a

an AI for learning. One computer is a
simulation engine where the AI could

simulation engine where the AI could

simulation engine where the AI could
learn how to be a robot in a uh in a

learn how to be a robot in a uh in a

learn how to be a robot in a uh in a
virtual environment. And then also the

virtual environment. And then also the

virtual environment. And then also the
deployment of it. Everything that moves

deployment of it. Everything that moves

deployment of it. Everything that moves
will be

will be

will be
robotic. As we put these robots into the

robotic. As we put these robots into the

robotic. As we put these robots into the
factories, remember the factories are

factories, remember the factories are

factories, remember the factories are
also robotic.

also robotic.

also robotic.
Today's factories are so incredibly

Today's factories are so incredibly

Today's factories are so incredibly
complex. This is

complex. This is

complex. This is
Delta's manufacturing line and they're

Delta's manufacturing line and they're

Delta's manufacturing line and they're
getting it ready for a robotic future.

getting it ready for a robotic future.

getting it ready for a robotic future.
It is already robotics and software

It is already robotics and software

It is already robotics and software
defined and now in the future there will

defined and now in the future there will

defined and now in the future there will
be robots working in it. In order for us

be robots working in it. In order for us

be robots working in it. In order for us
to create robots and design robots that

to create robots and design robots that

to create robots and design robots that
operate in and as a fleet, as a team,

operate in and as a fleet, as a team,

operate in and as a fleet, as a team,
working together in a factory that is

working together in a factory that is

working together in a factory that is
also robotic, we have to give it

also robotic, we have to give it

also robotic, we have to give it
omniverse to learn how

omniverse to learn how

omniverse to learn how
to work together. And that digital twin,

to work together. And that digital twin,

to work together. And that digital twin,
you now have a digital twin of the

you now have a digital twin of the

you now have a digital twin of the
robot. You have a digital twin of all of

robot. You have a digital twin of all of

robot. You have a digital twin of all of
the equipment. You're going to have

the equipment. You're going to have

the equipment. You're going to have
digital twin of the factory. Those

digital twin of the factory. Those

digital twin of the factory. Those
nested digital twins are going to be

nested digital twins are going to be

nested digital twins are going to be
part of what Omniverse is able to do.

part of what Omniverse is able to do.

part of what Omniverse is able to do.
These are all digital twins. They're all

These are all digital twins. They're all

These are all digital twins. They're all
simulations. They just look beautiful.

simulations. They just look beautiful.

simulations. They just look beautiful.
The image just looks beautiful, but it's

The image just looks beautiful, but it's

The image just looks beautiful, but it's
they're all digital twins. TSMC is

they're all digital twins. TSMC is

they're all digital twins. TSMC is
building a dig digital twin of their

building a dig digital twin of their

building a dig digital twin of their
next fab.

As we

As we
speak, there are five trillion dollars

speak, there are five trillion dollars

speak, there are five trillion dollars
of plants being planned around the world

of plants being planned around the world

of plants being planned around the world
over the next three years. Five trillion

over the next three years. Five trillion

over the next three years. Five trillion
dollars of new plants because the world

dollars of new plants because the world

dollars of new plants because the world
is reshaping because

is reshaping because

is reshaping because
re-industrialization moving around the

re-industrialization moving around the

re-industrialization moving around the
world. New plants are being built

world. New plants are being built

world. New plants are being built
everywhere. This is an enormous

everywhere. This is an enormous

everywhere. This is an enormous
opportunity for us to make sure that

opportunity for us to make sure that

opportunity for us to make sure that
they build it well and cost effectively

they build it well and cost effectively

they build it well and cost effectively
and on time. And so putting everything

and on time. And so putting everything

and on time. And so putting everything
into a digital twin is really a great

into a digital twin is really a great

into a digital twin is really a great
first step and preparing it for a

first step and preparing it for a

first step and preparing it for a
robotic future. In fact, building that

robotic future. In fact, building that

robotic future. In fact, building that
$5 trillion doesn't include a new type

$5 trillion doesn't include a new type

$5 trillion doesn't include a new type
of factory that we're building. And even

of factory that we're building. And even

of factory that we're building. And even
our own factories, we put in a digital

our own factories, we put in a digital

our own factories, we put in a digital
twin. This is the Nvidia AI factory in a

twin. This is the Nvidia AI factory in a

twin. This is the Nvidia AI factory in a
digital

digital

digital
twin. Gong is a digital twin. They made

twin. Gong is a digital twin. They made

twin. Gong is a digital twin. They made
Gausong a digital twin.

Gausong a digital twin.

Gausong a digital twin.
There are already hundreds of thousands

There are already hundreds of thousands

There are already hundreds of thousands
of buildings, millions of miles of

of buildings, millions of miles of

of buildings, millions of miles of
roads. And so, yes, Gaus is a digital

roads. And so, yes, Gaus is a digital

roads. And so, yes, Gaus is a digital
twin. We are at a once- ina-lifetime

twin. We are at a once- ina-lifetime

twin. We are at a once- ina-lifetime
opportunity. It is not it is not an

opportunity. It is not it is not an

opportunity. It is not it is not an
understatement to say that the

understatement to say that the

understatement to say that the
opportunity ahead of us is

opportunity ahead of us is

opportunity ahead of us is
extraordinary. For the very first time

extraordinary. For the very first time

extraordinary. For the very first time
in all of our time together, not only

in all of our time together, not only

in all of our time together, not only
are we

are we

are we
creating the next generation of IT,

creating the next generation of IT,

creating the next generation of IT,
we've done that several times from PC to

we've done that several times from PC to

we've done that several times from PC to
internet to cloud to mobile cloud. We've

internet to cloud to mobile cloud. We've

internet to cloud to mobile cloud. We've
done that several times. But this time,

done that several times. But this time,

done that several times. But this time,
not only are we creating the next

not only are we creating the next

not only are we creating the next
generation of IT, we are in fact

generation of IT, we are in fact

generation of IT, we are in fact
creating a whole new industry. This

creating a whole new industry. This

creating a whole new industry. This
whole new

whole new

whole new
industry is going to expose us to giant

industry is going to expose us to giant

industry is going to expose us to giant
opportunities ahead. I look forward to

opportunities ahead. I look forward to

opportunities ahead. I look forward to
partnering with all of you on building

partnering with all of you on building

partnering with all of you on building
AI factories, agents for enterprises,

AI factories, agents for enterprises,

AI factories, agents for enterprises,
robots, all of you amazing partners,

robots, all of you amazing partners,

robots, all of you amazing partners,
building the ecosystem with us around

building the ecosystem with us around

building the ecosystem with us around
one architecture. And so I want to thank

one architecture. And so I want to thank

one architecture. And so I want to thank
all of you for coming today. Have a

all of you for coming today. Have a

all of you for coming today. Have a
great Computex everybody.

great Computex everybody.

great Computex everybody.
[Applause]