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foreign speech. Those home journey
[Applause]
ka. Welcome to the stage, Nvidia founder
and CEO, Jensen Wong.
[Music]
It's great to be here. My parents are
also in the
audience. They're up
there. Nvidia has been coming to Taiwan
for over 30 years. This is the home of
many of our
treasured partners and dear
friends. Over the years, you have seen
Nvidia grow up and seen us
accomplished many exciting things and
have been partnered with me all along
the way.
Today we're going to talk
about where we
are in the industry, where we're going
to
go, announce some new products, exciting
new products and surprising products
that open new markets for
us, creates new markets, new growth.
We're going to talk
about great partners and how we're going
to develop this ecosystem
together. As you know, we are at the
epicenter of the computer
ecosystem, one of the most important
industries of the
world. And so it stands to
reason when new markets has to be
created, we have to create it starting
here.
at the center of the computer
ecosystem. And I have
some surprises for
you, things that you probably wouldn't
have
guessed. And then of course, I promise
I'll talk about
AI and we'll talk about
robotics. The NVIDIA
story is the reinvention of the computer
industry.
In fact, the NVIDIA story is also the
reinvention of our
company. As I said, I was been coming
here for 30
years. Many of you have been through
many of my
keynotes. Some of you, all of
them. And just as you reflect on the
conversation, the things we talked about
in the last 30 years, how dramatic we
changed. We started out as a chip
company with a goal of creating a new
computing
platform and in
2006 we
introduced
CUDA which has revolutionized how
computing is done. In 2016, 10 years
later, we realized that a new computing
approach has arrived. And this new
computing
approach requires a reinvention of every
single layer of the technology stack.
The processor is new, the software stack
is new. It stands to reason the system
is new. And so we invented a new system.
A new system that on the day I announced
it at GTC
2006, no one understood what I was
talking about and nobody gave me a
PO. That system was called
DGX1.
DGX1. I donated the first one to a
nonprofit company called
OpenAI and it started the AI
revolution. Years later, we realized
that in fact this new way of doing
software which is now called artificial
intelligence is unlike traditional ways
of running software. Whereas many
applications
ran on a few
processors in a large data center, we
call that hypers
scale. This new type of
application
requires many processors working
together serving queries for millions of
people and that data center would be
architected fundamentally different. We
realized there were two types of
networks. one for north south because
you still have to control the storage.
You still have to have a control plane.
You still have to connect to the
outside. But the most important network
was going to be east
west. The computers talking to each
other to try to solve a problem.
We recognized the most the best
networking company in east west traffic
for high performance computing largecale
distributed processing. A company that
was very dear to our company and very
close to our heart a company called
Melanox. And we bought them five years
ago
2019. We converted an entire data center
into one computing unit. And you heard
me say
before, the modern computer is an entire
data center. The data center is a unit
of computing. No longer just a PC, no
longer just a server. The entire data
center is running one job and the
operating system would
change. Nvidia's data center journey is
now very well
known. Over the last three years, you've
seen some of the ideas that we're
shaping and how we are starting to see
our company
differently. No company in history,
surely no technology company in history
has ever revealed a road map for five
years at a
time. No one would tell you what is
coming
next. They keep it as a secret,
extremely confidential.
However, we realized that Nvidia is not
a technology company only
anymore. In fact, we are an essential
infrastructure company. And how can you
plan your
infrastructure, your land, your shell,
your power, your electricity, all of the
necessary financing around all over the
world? How would you possibly do that if
you didn't understand what I was going
to
make? And so we described our company's
roadmap in fair detail. Enough detail
that everybody in the world can go off
and start building data
centers. We realize now we are an AI
infrastructure
company. An infrastructure company
that's essential all around the world.
Every region, every industry, every
company will build these
infrastructures. And what are these
infrastructure?
These infrastructure in
fact not unlike the first industrial
revolution when people realized GE
Westinghouse Seammens realized that
there was a new type of technology
called electricity and new
infrastructure has to be built all
around the world and these
infrastructure became essential part of
social infrastructure that
infrastructure is now called
electricity.
Years later, this is during all of our
generation, we realized there was a new
type of infrastructure. And this new
infrastructure was very conceptual, very
hard to understand. And this
infrastructure called
information. This information
infrastructure the first time it was
described make no sense to anybody. But
we now realized it is the internet. And
every internet is everywhere and
everything is connected to it.
Well, there's a new infrastructure. Now,
this new infrastructure is built on top
of the first two. And this new
infrastructure is an infrastructure of
intelligence. I know that right now when
we say there's an intelligence
infrastructure, it makes no sense. But I
promise you in 10 years time, you will
look back and you will realize that AI
has now integrated into everything. And
in fact we need AI everywhere and every
region, every industry, every country,
every company all needs AI. AI has now
part of infrastructure. And this
infrastructure just like the internet,
just like electricity needs
factories. And these factories are
essentially what we build today. They're
not data centers of the past. a1
trillion dollar industry providing
information and storage supporting all
of our ERP systems and our employees.
It's that's a data center a data center
of the past. This
is similar in the sense that it came
from the same industry. It came from all
of us but it's going to emerge as
something completely different
completely separated from the world's
data center. And these AI data centers,
if you will, are improperly described.
They are in fact AI factories. You apply
energy to it and it produces something
incredibly valuable. And these things
are called tokens to the point where
companies are starting to talk about how
many tokens they produced last quarter
and how many tokens they produced last
month. Very soon we will be talking
about how many tokens we produce every
hour just as every single factory does.
And so the world has fundamentally
changed. We went from a company on the
day that we started our
company. I was trying to figure out how
big our opportunity was in 1993 and I
came to the conclusion Nvidia's business
opportunity was enormous.
$300
million. We're going to be rich.
$300 million chip
industry to a data center opportunity
that represents about a trillion dollars
to now an AI factory and AI
infrastructure industry that will be
measured in trillions of
dollars. And this is the exciting future
that we're
undertaking. Now at its
core, everything we do is founded on
several important technologies. Of
course, I talk about accelerated
computing a great deal. I talk about AI
a great deal. What makes Nvidia really
special is the fusion of these
capabilities and very specially very
especially the algorithms, the
libraries, what we call the CUDA X
libraries. We're talking about libraries
all the time. And in fact, we're the
only technology company in the world
that talks about libraries
non-stop. And the reason for that is
because libraries is at the core of
everything that we do. Libraries is what
started it all. And I'm going to show
you a few new ones today. But before I
do
that, let me show you a preview of what
I'm going to tell you today. Everything
you're about to see, everything you're
about to see is
simulation, science, and artificial
intelligence. Nothing you see here is
art. It's all simulation. It just
happens to be beautiful. Let's take a
look.
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Heat.
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Heat. Heat. Heat.
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Heat.
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Heat.
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Heat. Heat.
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[Applause]
This is a real-time computer graphics
I'm standing in front
of. This is not a video. This is
computer graphics is generated by
GeForce. This is a brand new GeForce
5060 RTX
5060. And this this is from Asus. My
good friend Johnny is in the front row.
And this this is from MSI. And we took
this incredible GPU and we shrunk it in
here. Does that make any
sense? See, this is incredible. And so
this is this is MSI's new uh laptop with
5060 in it.
GeForce brought CUDA to the
world. Right now, what you're seeing is
every single pixel is ray
traced. How is that possible that we're
able to simulate photon and deliver this
kind of frame rate at this resolution?
Well, the reason for that is artificial
intelligence. We are only rendering
we're only rendering one out of 10
pixels.
So every pixel that you see only one out
of 10 is actually computed. The other
nine AI
guessed. Does that make any
sense? And it's
perfect. It's completely perfect. It
guessed it perfectly. Of course, the
technology is called DLSS, neural
rendering. It took us many, many years
to develop. We started developing it the
moment we started working on AI. So it's
been a 10-year journey. And the advance
in computer graphics has been completely
revolutionized by AI. GeForce brought AI
to the world. Now AI came back and
revolutionized GeForce. So really,
really amazing. Ladies and gentlemen,
Chief
Force, you know, when you're CEO, you
have many
children. And and GeForce brought us
here. And now all of our keynotes is 90%
not
GeForce. But it's not because we don't
love GeForce.
GeForce RTX 50 series just had its most
successful launch ever, the fastest
launch in our history, and PC gaming is
now 30 years old. So that tells you
something about how incredible GeForce
is. Let's talk about
libraries. At the core, of course,
everything starts with CUDA. And by
making CUDA as as uh performant as
possible, as pervasive as possible so
that the installed base is all over the
world, then applications can find a CUDA
GPU quite easily. The larger the
installed base, the more developers want
to create libraries. The more
libraries, more amazing things are done,
better applications, more benefits to
users. They buy more computers. The more
computers, more CUDA. That feedback path
is vitally important. However,
accelerated computing is not general
purpose computing. General purpose
computing write software. Everybody
writes it in, you know, Python or C or
C++ and you compile it. The methodology
for general purpose computing is
consistent throughout. Write the
application, compile the application,
run it on the CPU. However, that
fundamentally doesn't work in
accelerated computing because if you
could do that, it would be called a
CPU. What's the point of not of not just
changing the CPU? So, you could have
write the software, compile the
software, run it on a CPU. The fact that
you would have to do something different
is actually quite sensible. And the
reason for that is because so many
people worked on general purpose general
purpose computing, trillions of dollars
of innovation. How is it possible that
all of a sudden a few widgets inside a
chip and all of a sudden computers
become 50 times faster, 100 times
faster? That makes no sense. And so the
logic that we applied is that we could
accelerate applications if you
understood more about it. You can add
you can accelerate applications if you
were to create a architecture that are
better suited to accelerate to run at
the speed of light 99% of the runtime
and just even though it's only 5% of the
code which is quite surprising most
applications small small parts of the
code consumes most of the runtime. We
made that observation and so we went
after one domain after another. I just
showed you computer graphics. We also
have numeric. This is Koopai numeric.
This is uh Koopai is the most uh most
pervasive numerical library. Aerial and
Shona. Aerial is the world's first GPU
accelerated radio signal processing for
5G and 6G. Once we make it
softwaredefined, then we can put on top
of it AI. So now we could bring AI to 5G
and 6G. Pars for genomics analysis.
Monai for medical imaging. Earth 2 for
weather prediction. Coup quantum for
quantum
classical computer architectures and
computer systems. CU equariance and coup
tensor contraction of tensor uh
mathematics. Uh Megatron this whole row
this whole column here consists of all
of our deep learning and all of our
libraries necessary for training as well
as inference uh for deep learning. just
revolutionized computing and it all
started with all these libraries not
just CUDA but KUDDNN on top of KUDDNN
there was Megatron Megatron then Tensor
RTLM and then now lately this brand new
operating system for large AI factories
Dynamo QDF for data frames like Spark
and SQL structure data can be
accelerated as well QML classical
machine learning warp a framework for a
Pythonic framework for describing CUDA
kernels incredibly successful
coup mathematical operations
optimizations things like traveling
salesperson uh the ability to
optimize highly constrained large number
of uh variables uh type of problems like
supply chain optimization this is an
incredible success I'm very excited
about co-opt coup DSS and coup sparse
for sparse structure simulators uh those
are used for CAE and CAD fluid dynamics
uh finite element analysis incredibly
important for EDAS and CAE industry and
then of course KU litho one of the most
important libraries for computational
lithography mask making takes could
easily take a month and that mask making
process is extremely computationally
intensive and now with coup litho we
could ex we could accelerate that
computation by 50 times 70 times as a
result this is going going to set the
the stage, open the world for applying
AI to lithography in the future. We have
great partners here. TSMC is using KU
Litho quite extensively. ASML, Synopsis,
excellent partners working with us in
Kitho. So the the libraries
themselves is what makes it possible for
us one domain of application after
another domain of science after another
domain of physics to be able to
accelerate those applications. But it
also opens up markets for us. We look at
particular regions and particular
markets and we say that area could
really be important to transform to the
new way of doing computing. If general
purpose computing after all these years
has run its course, why hasn't it run
its course in every single industry? So
one of the most important industries of
course is telecommunications.
Just as the world's cloud data centers
have now become softwaredefined, it
stands to reason that telecommunication
should also be software defined. And so
that's the reason why we've taken now
some six years to refine and optimize a
fully accelerated radio access network
RAN RANS stack that does incredible
performance for data rate per megawatt
or data weight data rate per watt. We
are now
on par with the state-of-the-art AS6.
And so once we could do that, once we
could achieve that level of performance
and functionality, then after that we
can layer on top AI. And so we have
great partners here. You could see
SoftBank and T-Mobile, Indoad and
Vodafone are doing trials. Nokia,
Samsung, Kiosera are working with us on
the full stack. Fujitsu and Cisco are
working on the systems. And so now we
have the ability to
introduce the idea of AI on 5G or AI on
6G um along with uh AI on
computing. We're doing that with quantum
computing. Quantum computing is still at
the noisy intermediate state
intermediate scale quantum called NISK.
However, there are many many good
applications we could already started to
do and so we're excited about that.
We're working on a class a quantum
classical or quantum GPU computing
platform. We call it CUDAQ working with
amazing companies around the
world. GPUs could be used for
pre-processing and post-processing for
error correction for control. And so in
the future I predict that all
supercomputers will have quantum
accelerators. All have quantum QPUs
connected to it. And so a supercomputer
would be a QPU with QPUs and GPUs and
some CPUs. And that would be the
representation of a modern computer. So
working with a lot of great companies in
this area.
AI 12 years ago, we started with
perception AI models that can understand
patterns, recognize speech, recognize
images. That was the beginning. The last
five years, we've been talking about
generative AI, the ability for AI to not
just understand, but to generate. And
so, it could generate from text to text.
We use that all the time in chatbt. text
to images, text to video, video to text,
images to
text, almost anything to anything. Which
is the really amazing thing about AI
that we've discovered a universal
function approximator, a universal
translator. It can translate from
anything to anything else if we can
simply tokenize it, represent the uh the
bits of information. Well, now we have
reached a level of AI that's really
important.
Generative AI gave us oneshot AI. You
give a text and it gives you text back.
That was the two years ago when we first
engaged Chat GPT. That was the big
amazing breakthrough. You give a text
and it gives you text back. It predicts
the next word, predicts the next
paragraph.
However, intelligence is much more than
just what you've learned from a lot of
data that you've studied. Intelligence
includes the ability to reason, to be
able to solve problems that you've not
seen before, to break it down step by
step, to maybe apply some rules and
theorems to solve a problem you've never
seen. To be able to simulate multiple se
multiple options and weigh its
benefits. Some of the technology you
might have heard about chain of thought
breaking it down step by step tree of
thought coming up a whole bunch of
coming up a whole bunch of paths all of
these technologies are leading it
leading uh the ability for AI to be able
to reason now the amazing thing is once
you have the ability to reason and you
have the ability to perceive that is
let's say multimodal read PDFs you could
do search you can use tools you have now
a gentic AI
This agentic AI just does something that
I've just described all of us do. We
take we're given a goal. We break it
down step by step. We reason about what
to do, what's the best way to do it. We
consider its consequences and then we
start executing the plan. The plan might
include doing some research, might
include doing some work as using some
tools. It might include reaching out to
another AI agent to collaborate with it.
Agentic
AI is basically understand, think and
act. Well, understand, think, and act is
the robotics loop. Agentic AI is
basically a robot in a digital form.
These are going to be really important
in the coming years. We're seeing
enormous progress in this area. The next
wave beyond that is physical AI. AI that
understands the world. They understand
things like inertia, friction, cause and
effect. That if I if I roll a ball and
it goes under a car, depending on the
speed of the ball, it probably went to
the other side of the car, but the ball
did not disappear. Object permanence.
You might be able to reason that if
there's a table in front of you and you
have to go, you have to go to the other
side. The best way to do it is not to go
right through it. The best way is maybe
go around it or underneath it. to be
able to reason about these physical
things is really essential to the next
era of AI. We call that physical AI. And
so in this particular case, you're
seeing you're seeing us simply prompt
the AI and it generates videos to train
a self-driving car in different
scenarios. And I'll show you more of
that
later. That's a dog. It's a gener one
with a bird with people. And it started
out with the image on the
left. And then the the phase after that
we take reasoning
systems, generative
systems, physical AI, and this level of
capability would now go into a physical
embodiment. We call it a robot. If you
could imagine that you can prompt an AI
to generate a video to reach and pick
pick up a bottle. Of course, you could
imagine telling a robot to reach out and
pick up the bottle. The AI capability
today has the ability to do those
things. Well, the computer that we those
that's where we're going in the near
future. The computer that we're building
to make this possible has properties
that are very different than the
previous. The revolutionary computer
called Hopper came into the world about
3 years ago and it revolutionized AI as
we know it. It became probably the most
popular, most well-known computer in the
world. In the last several years, we
we've been working on a new computer to
make it possible for us to do inference
time scaling or basically thinking
incredibly fast because when you think,
you're generating a lot of tokens in
your head, if you will. You're
generating a lot of thoughts and you
iterate in your brain before you produce
the answer. So what used to be oneshot
AI is now going to be thinking AI,
reasoning AI, inference time scaling AI
and that's going to take a lot more
computation. And so we created a new
system called Grace Blackwell. Grace
Blackwell does several things. It has
the ability to scale up. Scale up means
to turn what is a computer into a giant
computer. Scale out is to take a
computer and connect many of them
together and let the work be done in
many different computers. Scaling out is
easy. Scaling up is incredibly
hard. Building larger
computers that is beyond the limits of
semiconductor physics is insanely hard.
And that's what Grace Blackwell does.
Grace Blackwell broke just about
everything. and all of you in in the
audience, many of you are partnering
with us to build Grace Blackwell
Systems. I'm so happy to say that we're
in full production, but I am also we can
also say it was incredibly challenging.
Although the bl the Blackwell systems
based on HGX has been in full production
since the end of last year and has been
available since February, we are now
just putting online all the great Grace
Blackwell systems. They're coming online
all over the place every single day.
It's available in coreweave now for
several weeks. It's already being used
by many CSPs and now you're starting to
see it coming up from everywhere.
Everybody start to tweet out that Grace
Blackwell is in for production. In Q3 of
this year, just as I promised, every
single year, we will increase the
performance of our platform every single
year like Rhythm. And this this year in
Q3 we'll upgrade to Grace Blackwell
GB300. The GB300 will increase the is
the same
architecture, same architecture, same
physical footprint, same electrical
mechanicals, but the chips inside have
been upgraded. It has upgraded with a
new black wall chip is now one and a
half times more inference performance,
has one and a half times more HBM memory
and it has two times more networking.
And so the overall system performance is
higher. Well, let's take a look at
what's inside Grace
Blackwell. Grace Blackwell
starts Grace Blackwell starts with this
compute node. this compute node right
here. This is one of the compute nodes.
This is um what the last generation
looks like, the B200. This is what B300
looks like. Notice right here in the
center, it's 100% liquid cooled now. But
otherwise, externally it's the same. You
could plug it into the same systems and
same chassis. And so this is the Grace
Blackwell GB300 system. It's one and a
half times more inference performance.
The training performance is about the
same but the inference performance is
one and a half times more. Now this
particular system here is 40 pedaflops
which is approximately the performance
of
the Sierra supercomput in
2018. The Sierra supercomput has 18,000
GPUs volta GPUs. This one node here
replaces that entire supercomput.
4,000 times increase in performance in
six
years. That is extreme Moore's
law. Remember, I've said before that AI,
Nvidia has been scaling computing by
about a million times every 10 years,
and we're still on that track. But the
way to do that is not just to make the
chips faster. There's only a limit to
how fast you can make chips and how big
you can make chips. In the case of
Blackwell, we even connected two chips
together to make it possible. TSMC
worked with us to invent a new co-ass
process called cos l that made it
possible for us to create these giant
chips. But still we want chips way
bigger than that. And so we had to
create what is called MVLink. This is
the world's fastest switch. This MVL
link here right here is 7.2 2 terabytes
per second. Nine of these go into that
rack. And that
nine, those nine switches are connected
by this miracle. This is um quite
heavy. That's because I'm quite strong.
I made it I made it look so light, but
this is almost this 70
pounds. And so this is the Envy link
spine.
Two miles of
cables, 5,000 cables
structured, all coaxed it impenes
matched and it connects all 72 GPUs to
all of the other 72 GPUs across this
network called MVLink
switch. 130 terabytes per second of
bandwidth across the MVLink spine. So
just put in
perspective the
peak traffic of the entire
internet the peak traffic of the entire
internet is
900 terra bits per
second. Divide that by
eight.
This moves more traffic than the entire
internet. one MVLink spine across this
MV nine of these MVLink switches so that
every single GPU can talk to every other
GPU at exactly the same time. This is
the miracle
of
GB200 and because there's a limit to how
far you can drive Certis. This is as far
as any Certis has ever driven from chip.
This goes chip to the switch out to the
spine to any other any other any other
switch any other chip all electrical.
And so that limit caused us to put
everything in one rack. That one rack is
120 kilowatts which is the reason why
everything has to be liquid cooled. We
now have the
ability to disagregate the
GPUs out of one motherboard essentially
across an entire rack. And so that
entire rack is one motherboard. That's
the miracle completely disagregated. And
now the GPU performance is incredible.
The amount of memory is incredible. The
networking bandwidth is incredible. And
now we can really scale these out. Once
we scale it up, then we can scale it out
into large systems. And notice almost
everything Nvidia builds are gigantic.
And the reason for that is because we're
not building data centers and servers.
We're building AI factories. This is
coreweave. This is Oracle cloud. The
power density of each rack is so great.
They have to they have to uh uh put them
further apart so that the power density
could be distributed. But really in the
end we're not building data centers.
We're building AI factories. And this is
the XAI Colossus factory. This is
Stargate. 4 million square feet. 4
million square feet. one gigawatt. And
so just think about this factory here.
This one gigawatt factory, this one
gigawatt factory is probably going to be
about, you know, 60 to 80 billion. Out
of that 60 to80 billion, the
electronics, the computing part of it,
these systems are 4050 billion dollars
of it. And so these are gigantic factory
investments.
The reason why people build factories is
because you know you know the answer.
The more you the more you
buy. Say it with me. The more you buy,
the more you
make. That's what factories
do. Okay.
The technology is so complicated. The
technology is so complicated. And in
fact, just looking at it here, you still
cannot get the deep appreciation of the
amazing work that's being done in all of
our partners and all of the companies
here in the audience in Taiwan. And so
we made you a movie. I made you a movie.
Take a look.
[Music]
Blackwell is an engineering
marvel. It begins as a blank silicon
wafer at
TSMC. Hundreds of chip processing and
ultraviolet lithography steps build up
each of the 200 billion transistors
layer by layer on a 12in wafer.
The wafer is scribed into individual
blackwell dye, tested and sorted,
separating the good dyes to move
forward. The chip on wafer on substrate
process done at TSMC spill and amp core.
Attaches 32 Blackwell dyes and 128 HBM
stacks on a custom silicon interposer
wafer. Metal interconnect traces are
etched directly into it, connecting
Blackwell GPUs and HBM stacks into each
system and package unit, locking
everything into place. Then the assembly
is baked, molded, and cured, creating
the Blackwell B200 Super Chip. At KYC,
each Blackwell is stress tested in ovens
at 125° C and pushed to its limits for
several hours.
Back at Foxcon, robots work around the
clock to pick and place over 10,000
components onto the Grace Blackwell
PCB. Meanwhile, additional components
are being prepared at factories across
the globe. Custom liquid cooling copper
blocks from Cooler Master, ABC, Auras,
and Delta keep the chips at optimal
temperatures.
At another Foxconom facility, Connect X7
Supernicks are built to enable scale out
communications and Bluefield 3DPUs to
offload and accelerate networking,
storage, and security
tasks. All these parts converge to be
carefully integrated into GB200 compute
trades.
MVLink is the breakthrough high-speed
link that Nvidia invented to connect
multiple GPUs and scale up into a
massive virtual
GPU. The MVLink switch tray is
constructed with MVLink switch chips
providing 14.4 terabytes per second of
all to all bandwidth.
MVLink spines form a custom blindmated
back plane integrating 5,000 copper
cables to deliver 130 tab per second of
all to-all bandwidth. This connects all
72 black wells or 144 GPU dyes into one
giant GPU from around the world. parts
arrive from Foxcon, Wistrun, Quanta,
Dell, Asus, Gigabyte, HPE, Super Micro
and other partners to be assembled by
skilled technicians into a rack scale AI
supercomput in total 1.2 million
components, 2 m of copper cable, 130
trillion transistors, weighing 1,800 kg.
[Music]
From the first transistor etched into a
wafer to the last bolt fastening the
Blackwell rack, every step carries the
weight of our partners' dedication,
precision, and
craft. Blackwell is more than a
technological wonder. It's a testament
to the marvel of the Taiwan technology
ecosystem.
[Music]
We couldn't be
[Applause]
prouder of what we've achieved
[Music]
together. Thank you, Taiwan.
Thank
you. Thank you. That was pretty
incredible, right? But that was
you. That was you. Thank
you. Well,
Taiwan doesn't
just build supercomputers for the world.
Today I'm very happy to
announce that we're also building AI for
Taiwan. And so today we're announcing
that Foxcon,
Taiwan, the Taiwanese government,
Nvidia, TSMC, we're going to build the
first giant AI supercomputer here for
the AI infrastructure in the AI
ecosystem of Taiwan. Thank you.
Is there anybody who needs an AI
computer?
Any AI researchers in the audience?
every single student, every researcher,
every scientist, every
startup, every large established
company. TSMC themselves does enormous
amounts of AI and scientific research
already. And so, Foxcon does enormous
amount of work in robotics. I know that
there are many other companies in the
audience. I'm going to mention you in
just a second that are doing robotics
research and AI research. And so having
a worldclass AI infrastructure here in
Tai in Taiwan is really
important. All of that is so that we
could build a very large
chip. and MVLink and Blackwell this
generation made it possible for us to
create these incredible systems. Here's
one from Pegatron and QCT and Wishron
and we win. This is from Foxcon and
Gigabyte and ASUS. And you could see the
front and the back of it.
And its entire goal, its entire goal is
to take these Blackwell chips that are,
you know, you could see how big they are
and turn it into one massive chip. Now
the ability to do that of course made
was made possible by MVLink but it
understates the complexity of the system
architecture the rich software ecosystem
that connects it all together the entire
ecosystem of 150 companies that came
together to build this this
architecture and the entire ecosystem in
technology in software in industry
has been the work of three
years. This is a massive industrial
investment and now we would like to make
it possible for anybody anybody who
wants to build data centers. It could be
a whole bunch of NVIDIA GB200s or 300s
and an accelerated computing systems
from Nvidia. It could be somebody else.
And so today we're announcing something
very
special. We're announcing NVIDIA MVLink
Fusion. MVLink
Fusion is so that you can
build
semicustom AI
infrastructure, not just semi-custom
chips, because those are the good old
days. You want to build AI
infrastructure and everybody's AI
infrastructure could be a little
different. Some of you could have a lot
more CPUs and some of it could have a
lot more Nvidia GPUs and some of it
could be somebody's semi-custom AS6. And
those systems are so insanely hard to
build and they're all missing this one
incredible ingredient. This incredible
ingredient called MVLink. MVLink so that
you could scale up these semi-custom
systems and build really powerful
computers. And so today we're announcing
the MVLink Fusion. MVLink Fusion kind of
works like this. This is the Nvidia
platform 100% Nvidia. You got Nvidia
CPU, Nvidia GPU, the MVLink
switches, the networking from Nvidia
called Spectrum X or
Infiniband,
Nyx, network
interconnects,
switches and all of the entire system,
the entire infrastructure built end to
end. Now, of course, you could mix and
match it if you like, and we now today
make it possible for you to mix and
match it even at the compute level. This
would be what you would do using your
custom
ASIC. And we have great partners, I'll
announce in a second, who are working
with us to integrate your special TPU or
your special ASIC, your special
accelerator. And it doesn't have to be
just a transformer accelerator. It could
be an accelerator of any kind that you
would like to integrate into a large
scaleup
system. We create an MVLink chiplet.
It's basically a switch that a butts
right up to your
chip. There's IP that will be available
to integrate into your semi-custom ASIC.
And then once you do that, it fits right
into the compute boards that I mentioned
and it fits into this m
this ecosystem of an AI supercomputer
that I've shown you. Now maybe what you
would like is you would like to use your
own CPU. You've been building your own
CPU for some time and maybe your CPU has
built a very large ecosystem and you
would like to integrate Nvidia into your
ecosystem. And now we make it possible
for you to do that. You could do that by
building a your custom CPU. We provide
you with our MVLink chip to chip
interface into your ASIC. We connect it
with MVLink chiplets. And now it
connects and directly abuts
into the Blackwell chips and our next
generation Ruben chips. And again, it
fits right into this ecosystem. This inc
incredible body of work now becomes
flexible and open for everybody to
integrate into. And so your AI
infrastructure could have some Nvidia, a
lot of yours, a lot of yours, you know,
and and uh a lot of CPUs, a lot of AS6,
maybe a lot of Nvidia GPUs as well. And
so in any case, you have the benefit of
using the MVLink infrastructure and the
MVLink ecosystem that's and is connected
perfectly to Spectrum
X and all of that you know is industrial
strength and has the benefit of an
enormous ecosystem of industrial
partners who have already made it
possible. So this is the MVLink fusion.
Whether you buy completely from us,
that's fantastic. Nothing gives me more
joy than when you buy everything from
Nvidia. I just want you guys to know
that.
But it gives me tremendous joy if you
just buy something from
Nvidia. And so we have some great
partners. We have some great partners.
LCHIP, Astera Labs, Marll and one of our
great partners, MediaTek are going to be
partnering with us to work with ASIC or
semi-custom customers, hyperscalers who
would like to build these things or CPU
vendors who would like to build these
things and they would be their
semicustom as provider. We also have
Fujitsu and Qualcomm who are building
semi who are building their CPUs with
MVLink to integrate into our ecosystem
and Cadence and Synopsis. We've worked
with them to
integ available to all of your chips. So
this ecosystem is incredible. But this
just highlights the MVLink Fusion
ecosystem. Once you work with them, you
instantly get integrated into the entire
larger Nvidia ecosystem that makes it
possible for you to scale up into these
AI supercomputers. Now, let me talk to
you about some new product categories.
As you know, I've shown you a couple of
different
computers.
However, in order to serve the vast
majority of the world, there are still
some computers that are missing. And so,
I'm going to talk about them. But before
I do that, I want to give you an update
that in fact this new computer we call
DGX Spark is in full
production. DGX
Spark will be ready will be available
shortly probably in a few weeks. We have
tremendous partners working with us.
Dell,
HPI, Asus, MSI,
Gigabyte,
Lenovo, incredible partners with working
with us. And this is the DGX Spark. This
is actually a production unit. This is
our version. This is our version.
However, our partners are building a
whole bunch of different
versions. This is designed for AI native
developers. If you're a developer,
you're a student, you're a researcher,
and you don't want to keep opening up
the cloud and getting it prepared and
then when you're done scrubbing it,
okay, but you would just like to have
your own basically your own AI cloud
sitting right next to you and it's
always on, always waiting for you. It
allows you to do your prototyping, early
development. And this is what's amazing.
This is um DGX Spark. It's one
pedlops and 128
gigabytes. In
2016 when I delivered DGX1, this is just
the bezel. I can't lift a whole
computer. It's 300 lb. This is
DGX1. This is one
pedlops and 128
gigabytes. Of course, this is 128
gigabytes of HBM memory. And this is 128
gigabytes of
LPDDR5X. The performance is in fact
quite similar. But what's most important
is that the work that you could do you
could work on this is the same work you
could do here. It's an incredible
achievement over just the course of
about 10 years. Okay. So this is DGX
Spark for anybody who would like to have
their own AI supercomputer. And it's um
I'll let all of our partners price it
for themselves, but one thing for
sure, everybody can have one for
Christmas. Okay, I've got another
computer I want to show you. If that's
not If that's not
enough and you would still like to have
your own personal Thank you, Janine.
This is Janine Paul, ladies and
gentlemen.
If that one isn't big enough for
you, here's one. This is another desk
side. This is also going to be available
from Dell and HPI, Asus, Gigabyte, MSI,
Lenovo. Uh it'll be available from Box,
from Lambda, amazing workstation
companies. And this is going to be your
own personal
DGX supercomput.
This computer is the most performance
you can possibly get out of a wall
socket. You could put this in your
kitchen, but just
barely. If you put this in your kitchen
and then somebody runs the microwave, I
think that's the limit. And so this is
the limit. This is the limit of what you
can get out of a wall outlet. And this
is a DGX station. The programming model
of this and the giant systems that I
showed you are the same. That's the
amazing thing. One architecture, one
architecture. And this has the ability,
enough capacity and performance to run a
1 trillion parameter AI model. Remember
Llama is Llama 70B. A one trillion
parameter model is going to run
wonderfully on this machine. Okay, so
that's the DGX station. So now let's
talk
about remember these systems are Thank
you, Jenny. These systems these systems
are AI
natives. They're AI native computers.
They're computers built for this new
generation of
software. It doesn't have to be x86
compatible. It doesn't have to run
traditional IT
software. It doesn't have to run
hypervisors. It doesn't have to run all
of the It doesn't have to run Windows.
These computers are designed for the
modern AI native
applications. Of course, these AI
applications could be APIs that can be
called upon by the tra traditional and
the classical applications. But in order
for us to bring AI into a new world and
this new world is enterprise IT, we have
to go back to our roots and we have to
reinvent computing and bring AI into
traditional enterprise computing. Now
enterprise computing as we know is
really three layers. It's not just the
computing layer. It's compute, storage,
and networking. It's always compute,
storage, and networking. And just as AI
has changed everything, it stands to
reason that AI must have changed
compute, storage, and networking for
enterprise IT as well. Well, that lower
layer has to be completely reinvented,
and we're in the process of doing that.
I'm going to show you some new products
that opens up, unlocks enterprise IT for
us. It has to work with the traditional
IT industry and it has to add a new
capability and the new
capability for enterprise is agentic AI.
basically
digital marketing campaign manager, a
digital researcher, a digital software
engineer, digital customer service,
digital chip designer, digital supply
chain
manager, digital versions, AI versions
of all of the work that we used to do.
And as I mentioned earlier, Agentic AI
has the ability to reason, use tools,
work with other AIs. So in a lot of
ways these are digital workers. They're
digital
employees. The world has a shortage of
labor. We have a shortage of workers by
2030 by about 30 to 50 million shortage.
It's actually limit limiting the world's
ability to grow. And so now we have
these digital agents that can work with
us. 100% of NVIDIA software engineers
now have digital agents working with
them so that they can help them assist
them in developing better code and more
productively. And so in the future,
you're going to have this layer. That's
our vision. You're going to have a layer
of agentic AIs, AI agents. And so what's
going to happen to the world? What's
going to happen to enterprise? Whereas
we have HR for human workers, we're
going to have it becoming the HR of
digital workers.
And so we have to create the necessary
tools for today's IT industry, today's
IT workers to be able to
manage, improve, evaluate a whole family
of AI agents that are working inside
their company. And so that's the vision
of what we want to build. But first, we
have to reinvent computing. Remember
what I said, enterprise it works on x86.
It runs traditional software such as
hypervisors from VMware or IBM Red Hat
or Nanix. It runs a whole bunch of
classical applications and we need to
have computers that do the same thing
while it adds this new
capability while it adds this new
capability called agent AI. And so let's
take a look at that.
Okay, this is
This is the brand new RTX Pro. RTX Pro
Enterprise and Omniverse server. This
server can run everything. It has x86 of
course. It can run all of the classical
hypervisors. It runs Kubernetes in those
hypervisors. So the way that your IT
department wants to manage your network
and how how they want to manage your
your clusters and orchestrate workload
works exactly the same way. It has the
ability to even stream Citrix and what
other what other virtual desktops to
your to your PC. Everything that runs in
the world today should run here.
Omniverse runs on here perfectly. But in
addition to that in addition to that
this is the computer
for enterprise AI
agents. Those AI agents could be only
text. Those AI agents could also be
computer
graphics, little TJs, you know, coming
to you, little toy Jensen's coming to
see you, you know, helping you do work.
And so those AI agents could be either
in text form, it could be in graphics
form, it could be in video form. All of
those workloads work on this system. No
matter the modality, every single model
that we know of in the world, every
application that we know of should run
on this. In fact, even Crisis works on
here. Okay, so anybody who's a GeForce
gamer, there are no GeForce gamer in the
room. Okay, what connects these eight
GPUs? The Blackwell, new Blackwell RTX,
RTX Pro 6000s is this new motherboard.
This new motherboard is actually a
switched network. CX8 is a new category
of chips. It's a switch first,
networking chip second. It's also the
most advanced networking chip in the
world. This is now in volume production
CX8. In the CX8, you plug you plug in
the GPUs. The CX8s are in the back.
PCI Express connected here. CX8
communicates between them and the
networking bandwidth is incredibly high
at 800 gigabits per second. And this is
the transceiver that plugs into here. So
each one of these GPUs have their own
networking interface. All of the GPUs
are now communicating to all of the
other GPUs on east west traffic.
Incredible performance. Now the
surprising part is this. How incredible
it is.
So this is this is um RTX
Pro. This is the performance and I
showed you guys at GTC how to think
about performance in the world of AI
factories. The way to think about this
is throughput. This is tokens per second
which is the y- axis. The more output
your factory, the more tokens you
produce. Okay? So throughput is
measuring tokens per second. However,
every AI model is not the same and some
AI models require much more reasoning
and so you need those AI models, you
need the performance per user to be very
high. So the tokens per second per user
has to be high. And this is the problem
with factories. Factories either like to
have high throughput or low latency, but
it doesn't like to have both. And so the
challenge is how to create an operating
system that allows us to have high
throughput, the y axis, while having
very low latency, which is the y- axis,
interactivity, tokens per second per
user. And so this chart tells you
something about the overall performance
of the computer, of the overall comput
of the
factory. Look at all those different
colors. It reflects on it represents the
different ways you have to configure all
of our GPUs to achieve the performance.
Sometimes you need pipeline parallelism,
sometimes you want expert parallelism,
sometimes you want to batch, sometimes
you want to do spec speculative
decoding, sometimes you don't. And so
all of those different types of
algorithms have to be applied separately
and differently depending on the
workload. and the paro the outside area
the overall area of that curve represent
the capability of your factory. Okay.
And so notice something Hopper is
our this is the most famous computer in
the world. Hopper H100. The HGX
$225,000 is Hopper is down there. And
the Black Wall server you just saw, the
enterprise server is 1.7 times its
performance. But this is amazing. This
is Llama 70B. This is DeepSseek R1.
Deepseek R1 is four times. Now the
reason for that of course is Deepseek R1
has been optimized and this is DeepSec
R1 is genuinely a gift to the world's AI
industry. the the amount of computer
science breakthrough is is really quite
significant and has really opened up a
lot of great research for uh researchers
in United States around the world
everywhere I go research R1 has made a
real impact in how people think about
think about AI and how think about
inference and how to think about
reasoning AIs they've made a great
contribution to uh to the industry and
to the world and so this is deepseek R1
the performance is four times the
state-of-the-art H100 that kind of puts
it in perspective. Okay? And so if
you're building enterprise AI, we now
have a great server for you. We now have
a great system for you. It's a computer
you could run anything on. It's a
computer that has incredible performance
and it, you know, whether it's x86 or
AI, all of it runs. Okay, it's going to
it's a our RTX Pro server is in volume
production across all of our partners in
the industry. This is likely the largest
go-to market of any system we have ever
taken to market. So, thank you very
much. The compute platform is different.
The storage platform is different. And
the reason for that is because humans
query structured databases like
SQL. People query structured databases
like SQL. But AI wants to query
unstructured data. They want semantic.
They want meaning. And so we have to
create a new type of storage platform.
And this is the NVIDIA AI data platform
on top just as a just as SQL servers,
SQL software and file storage software
from your storage vendors that you work
with. There's a layer of very
complicated software that goes with
storage. Most storage companies, as you
know, is mostly a software company.
That software layer is incredibly
complicated. And so on top of a new type
of storage system is going to be a new
query system we call IQ, Nvidia AIQ or
IQ. And this it's really
state-of-the-art. It's fantastic. And
working with basically everybody in the
storage industry. Your future storage is
no longer CPUs sitting on top of a rack
of storage. It's going to be GPUs
sitting on top of a rack of storage. And
the reason for that is because you need
the system to embed find the meaning in
the data in the in the unstructured data
in the raw data. You have to index you
have to do the search and you do
ranking. So that process is very compute
intensive. And so most storage servers
in the future will have a compute a GPU
computing node in front of it. It's
based on the models that we create.
Almost everything that I'm about to show
you starts with great AI models. We
create AI models.
We put a lot of energy and technology
into post-training of open AI models. We
train we post train these AI models with
data that is completely transparent to
you. It is safe and secure data and uh
it's uh completely completely uh uh okay
to use to train and we make that list
available to you to see and so it's
completely transparent. We make the data
available to you. We post-train the
models and our post-trained model
performance is really incredible. It's
right now downloadable open-source
reasoning model. The llama neotron
reasoning model is the world's best.
It's been downloaded tremendously. It's
and also we also surround it with a
whole bunch of AI other AI models so
that you can do what is called IQ the
retrieval part of it. It's 15 times
faster than what's available out there.
50% better query results. And so these
models are available all available to
you. It's blueprints are the IQ
blueprint are open source and we work
with the storage industry to integrate
these models into their storage stack
their AI platform. This is Vast. This is
what it looks like. I'm not going to go
into it. I just want to give you a
texture of the AI models that are
integrated into their platform. Let's
take a look at what Vast has
done. Aentic AI changes how businesses
use data to make decisions.
In just 3 days, VAS built a sales
research AI agent using the NVIDIA IQ
blueprint and its accelerated AI data
platform. Using Nemo Retriever, the
platform continuously extracts, embeds,
and indexes data for fast semantic
search.
First, the agent drafts an
outline, then taps into CRM
systems, multimodal knowledge bases and
internal
tools. Finally, it uses Llama Neimatron
to turn that outline into a step-by-step
sales plan.
Sales planning that took days now starts
with an AI prompt and ends with a plan
in
minutes. With Vast's accelerated AI data
platform, organizations can create
specialized agents for every
[Music]
employee. Okay, so that's vast.
Dell has a great AI platform, one of the
world's leading storage vendors.
Hitachi has a great AI platform, AI data
platform. IBM is building an AI data
platform with NVIDIA Nemo. NetApp is
building a net AI platform. As you could
see, all of these are open to you. And
if you're building an AI platform with a
semantic query AI in front of it, Nvidia
Nemo is the world's best. Okay. So that
gives you now compute for enterprise and
storage for enterprise. The next part is
a new layer of software called AI ops.
Just as supply chain has their ops and
HR has their ops in the future it will
have AI ops and they will curate data.
They'll fine-tune the models. They'll
evaluate the models, guard rail the
models, secure the models, and we have a
whole bunch of libraries and models
necessary to integrate into the AI ops
ecosystem. We got great partners to help
us do that to take it to market for us.
Crowd Crowdstrike is working with us.
Data IQ is working with us. Data Robots
is working with us. You could see these
are all AI
operations creating fine-tuning models
and deploying models for agentic AI in
enterprise and you could see NVIDIA
libraries and models integrated all over
it. So data robots here's data
stacks this is elastic I think I heard
somewhere that they're downloaded 400
billion times. This is
Newonix. This is Red Hat. This is trend
micro here in Taiwan. I think I saw Eva
earlier. Okay. Hi, Eva. Okay. Wait some
biases. Okay. And so so that's it. This
is this is how we're going to bring the
world bring to the world's enterprise IT
the ability to add AI to everything that
you do. You're not going to rip out
everything from it the enterprise IT
organizations because companies have to
run. But we can add AI into it. And now
we have systems that are enterprise
ready with ecosystem partners,
incredible ecosystem partners. I think I
saw Jeff earlier. There's Jeff Clark,
the great Jeff Clark been coming to
Taiwan for as long as I have been coming
to Taiwan and has been a partner of all
yours for a long time. So there's Jeff
Clark. And and so our ecosystem
partners, Dell and others are going to
take this platform, these platforms uh
to the world's enterprise IT. Okay,
let's talk about robots.
So agent AIS agentic AIs AI agents a lot
of different ways to say it agents are
essentially digital robots re reason for
that is because a robot perceives
understands and plans and that's
essentially what agents do. But we would
like to build also physical robots and
these physical robots first it starts
with the ability to learn to be a robot.
The ability to learn to be a robot can't
be done in the physical world
productively. You have to create a
virtual world where the robot can learn
how to be a good robot. That ro that
virtual world has to obey the laws of
physics. Most physics engines don't have
the ability to with fidelity deal with
rigid and soft body simulation. And so
we partner with deep uh with uh with
DeepMind, Google DeepMind and Disney
Research to build Newton, the world's
most advanced physics engine. It's going
to be open sourced in
July. It's incredible what it can
do. It's completely GPU
accelerated. It's differentiable, so you
could learn from
experience. It is incredibly high
fidelity. It's super real
time and so we could use that new engine
and it's integrated into Mujoko. It's
integrated into Nvidia's Isaac sim. So
irrespective of the simulation
environment and framework you use. And
so with that we can bring these robots
to life.
[Music]
Queen.
[Music]
Who doesn't want that? I want
that. Can you imagine one of those
little ones or a few of them running
around the
house chasing your
dogs, driving them crazy? And so, did
you see what was happening? It wasn't an
animation. It was a simulation.
and he was slipping and sliding in the
sand in the dirt. All of it was
simulated. The software of the robot is
running in the simulation. And so it
wasn't animated. It was simulated. And
so in the future, we'll take the AI
models that we train and we put it into
that robot in simulation and let it
learn how to be a great robot. Well, our
we're working on several things to help
the robotics industry. Now, you know
that we've been working in in uh
autonomous systems for some time. Our
self-driving car basically has three
systems. There's the system for creating
the AI model, and that's GB 200, GB300.
It's going to be used for that, training
the AI model. Then you have Omniverse
for simulating the AI model. And then
when you're done with that AI model, you
put that model, the AI into the
self-driving car. Okay. This year we're
deploying Mercedes around the world, our
self-driving car stack, endto-end stack.
But we create all of this and all the
and the way we go to market is exactly
the same that we work everywhere else.
We create the entire stack. We open the
entire stack and for our partners, they
use whatever they want to use. They
could use our computer and not our
library. They could use our computer,
our library, and also our runtime.
However much you would like to use, it's
up to you because there's a lot of
different engineering teams and
different engineering styles and
different engineering capabilities. We
want to make sure that we provide our
technology in a way that makes it as
easy as possible for everybody to
integrate NVs technology. You know, like
I said, I love it if you buy everything
from me, but just please buy something
from
me. Very practical.
And so so we're doing exactly the same
thing in robotic systems just like cars.
And so this is our Isaac Groot platform.
The simulation is exactly the same as
Omniverse. The comput the training
system is the same. When you're done
with the model, you put it into inside
this Isaac group platform. And Isaac
Group platform starts with a brand new
computer called Jetson Thor. This is
just started in production. It is an
incredible processor. Basically a
robotic processor goes to self-driving
cars and it goes into a human or robotic
system. On top is an operating system we
called Isaac Nvidia Isaac. The Nvidia
Isaac operating system is the runtime.
It does all of the neuronet network
processing, the sensor processing
pipelines all of it and deliver actuated
results and then on top of it
pre-trained models that we created with
an crazy an amazing re uh amazing
robotics team uh that are pre-training
these models and uh all the tools
necessary in creating this we make
available including the model and so
today we're announcing that Isaac Groot
N1.5 is now open sourced and it's open
to the world to It's been downloaded
6,000 times already and the popularity
and the likes and the and the and the uh
appreciation from the community is
incredible. And so that's creating the
model. We also open the way we created
the model. The biggest challenge in
robotics is and well the biggest
challenge in AI overall is what is your
data
strategy and your data strategy has to
be that's where great deal of research
and a great deal of technology goes into
in the case of robotics human
demonstration just like we demonstrate
to our children or a a coach
demonstrates to an athlete you
demonstrate using tele
operations you demonstrate to the robot
how to perform the task and the robot
can generalize ize from that
demonstration because AI can generalize
and we have technology for
generalization. You can generalize from
that one demonstration other techniques.
Okay. And so what
if what if you want to teach this robot
a whole bunch of skills? How many
different teleoperation people do you
need? Well, it turns out to be a lot.
And so what we decided to do was you use
AI to
amplify the human demonstration systems.
And so this is essentially going from
real to real and using an AI to help
us expand amplify the amount of data
that was collected during human
demonstration to train an AI model.
Let's take a look.
The age of generalist robotics has
arrived with breakthroughs in
mechatronics, physical AI, and embedded
computing. Just in time, as labor
shortages limit worldwide industrial
growth, a major challenge for robot
makers is the lack of large-scale, real,
and synthetic data to train
models. Human demonstrations aren't
scalable, limited by the number of hours
in a day.
Developers can use NVIDIA Cosmos
physical AI world foundation models to
amplify
data. Group dreams is a blueprint built
on Cosmos for large-scale synthetic
trajectory data generation, a realtoreal
data
workflow. First, developers fine-tune
Cosmos with human demonstrations
recorded by tea operation of a single
task in a single environment.
Then they prompt the model with an image
and new instructions to generate dreams
or future world
states. Cosmos is a generative model so
developers can prompt using new action
words without having to capture new
teleop
data. Once a large number are
generated, Cosmos reasons and evaluates
the quality of each dream, selecting the
best for training. But these dreams are
still just pixels. Robots learn from
actions. The Groot Dreams blueprint
generates 3D action trajectories from
the 2D dream videos. This is then used
to train the robot model.
Group Dreams lets robots learn a huge
variety of new actions with minimal
manual captures. So, a small team of
human demonstrators can now do the work
of
thousands. Groot Dreams brings
developers another step closer to
solving the robot data
challenge. Is that great?
So in order for robotics to happen, you
need you need AI. But in order to teach
the AI, you need AI. And so this is
really the great thing
about the era of agents where we need a
a large amount of synthetic data
generation, robotics, a large amount of
synthetic data generation and skill
learning called fine-tuning, which is a
lot of reinforcement learning and
enormous amount of compute. And so this
is an er this is a whole era where the
training of these AI the development of
these as well as the running of the AI
needs an enormous amount of compute.
Well, as I mentioned earlier, the world
has a severe shortage of labor. And the
reason why humano robotics is so
important is because it is the only form
of robot that can be deployed almost
anywhere brownfield. It doesn't have to
be green field. It could fit into the
world we created. It could do the task
that we made for ourselves. We
engineered the world for ourselves and
now we could create a robot that fit
into that world to help us. Now the
amazing thing about human robotics is
not just the fact that if it worked it
could be quite versatile. It is likely
the only robot that is likely to work
and the reason for that is because
technology needs scale.
Most of the robotic systems we've had so
far are too low volume. And those low
volume systems will never achieve the
technology scale to get the flywheel
going far enough fast enough so that
we're willing to dedicate enough
technology into it to make it better.
But human or robot, it is likely to be
the next multi-trillion dollar industry.
And the technology innovation is
incredibly fast and the consumption of
computing and data centers enormous. But
this is one of those applications that
needs three computers. One computer is
an AI for learning. One computer is a
simulation engine where the AI could
learn how to be a robot in a uh in a
virtual environment. And then also the
deployment of it. Everything that moves
will be robotic. As we put these robots
into the factories, remember the
factories are also robotic.
Today's factories are so incredibly
complex. This is
Delta's manufacturing line and they're
getting it ready for a robotic future.
It is already robotics and software
defined and now in the future there will
be robots working in it. In order for us
to create robots and design robots that
operate in and as a fleet, as a team,
working together in a factory that is
also robotic, we have to give it
omniverse to learn how
to work together. And that digital twin,
you now have a digital twin of the
robot, you have a digital twin of all of
the equipment, you're going to have
digital twin of the factory. Those
nested digital twins are going to be
part of what Omniverse is able to do.
This is Delta's digital twin. This is
WiiWin's digital twin. Now, while you're
looking at this, if you're not if if you
look at it too closely, you think that
it's in fact photographs. These are all
digital twins. They're all simulations.
They just look beautiful. The image just
looks beautiful, but they're all digital
twins. This is Pegatron's digital twin.
This is Foxcon's digital
twin. This is Gigabytes digital
twin. This is
Quantis. This is
Wistrons. TSMC is building a dig digital
twin of their next
fab. As we speak, there are five
trillion dollars of plants being planned
around the world. Over the next three
years, $5 trillion dollars of new plants
because the world is reshaping because
re-industrialization moving around the
world. New plants are being built
everywhere. This is an enormous
opportunity for us to make sure that
they build it well and cost effectively
and on time. And so putting everything
into a digital twin is really a great
first step and preparing it for a
robotic future. In fact, building that
$5 trillion doesn't include a new type
of factory that we're building. And even
our own factories we put in a digital
twin. This is the Nvidia AI factory in a
digital
twin. Gong is a digital twin. They made
Gausong a digital
twin. They're already hundreds of
thousands of buildings, millions of
miles of roads. And so, yes, Gaus is a
digital twin. Let's take a look at all
of
this. Taiwan is pioneering
softwaredefined
manufacturing. TSMC, Foxcon, Wistron,
Pegatron, Delta Electronics, Quant,
Wiiwin, and Gigabyte are developing
digital twins on Nvidia Omniverse for
every step of the manufacturing process.
TSMC with MEAI generate 3D layouts of an
entire fab from 2DC
CAD and develop AI tools on
co-op that can simulate and optimize
intricate piping systems across multiple
floors saving months of time.
Quanta, Wistron, and Pegatron plan new
facilities and production lines
virtually prior to physical
construction, saving millions in costs
by reducing
downtime. Pegatron simulates solder
paste
dispensing, reducing production defects.
Quanta uses Seaman's Team Center X with
Omniverse to analyze and plan multi-step
processes. Foxcon, Wistron, and Quanta
simulate power and cooling efficiency of
test data centers with Cadence Reality
digital twin. and to develop physical AI
enabled robots. Each company uses its
digital twin as a robot gym to develop,
train, test, and simulate
robots, whether manipulators, AMRs,
humanoids, or vision AI agents as they
perform their tasks or work together as
a diverse
fleet. And when connected to the
physical twin with IoT, each digital
twin becomes a realtime interactive
dashboard. Pegatron uses NVIDIA
Metropolis to build AI agents who help
employees learn complex
techniques. Taiwan is even bringing
digital twins to its cities.
Linker Vision and the city of Kaos use a
digital twin to simulate the effects of
unpredictable
scenarios and build agents that monitor
city camera
streams, delivering instant alerts to
first
responders. The age of industrial AI is
here. Pioneered by the technology
leaders of
Taiwan. Powered by
[Music]
Omniverse. My entire keynote is your
work. It's so excellent.
Well, it stands to reason it stands to
reason that Taiwan at the center of the
most
advanced
industry, the epicenter where AI and
robotics is going to come from. It
stands to reason that this is an
extraordinary opportunity for
Taiwan. This is also the largest
electronics manufacturing region in the
world. And so it stands to reason that
AI and robotics will transform
everything that we do. And so it's
really quite extraordinary that for one
of the first times in history that the
work you
do has revolutionized every industry and
now it's going to come back to
revolutionize yours. At the beginning I
said that GeForce brought AI to the
world and then AI came
back and transformed GeForce.
You brought AI to the world. AI will now
come back and transform everything that
you do. It's been a great pleasure
working with all of you. Thank
you. I have a new product. I announced
several products already today, but I
have a new product to announce. I have a
new product to announce. We've been
building out in space doc for some
time and
um I think it's time for us to reveal
one of the largest products that we've
ever built. And it's uh parked outside
uh waiting for us. Let's uh let's see
how it
goes.
[Music]
[Applause]
Heat. Heat.
[Music]
Heat. Heat.
[Music]
[Applause]
[Music]
[Applause]
Nvidia constellation.
Nvidia Constellation. Well, as you know,
we have been growing and all of our
partnerships with
you have been growing. The number of
engineers we have here in Taiwan have
been
growing. And so, we are growing beyond
the limits of our current office.
And so I'm going to build them a brand
new Nvidia Taiwan office and it's called
Nvidia
Constellation. We've also been selecting
the
sites. We've been selecting the sites
and all of the mayors and all the
different cities have been very kind to
us
and I think we got some nice deals.
I'm not sure. Seems quite
expensive, but prime real estate is
prime real estate. And so today, today
I'm very pleased to announce that Nvidia
constellation will be at Beetho
Sheiling.
[Applause]
We have we have negotiated
uh the transfer of the lease from the
current current uh current owners of
that lease.
However, I understand that in order for
the mayor to approve that
lease, he wanted to know
whether the people of
Taipei approve of us building a large,
beautiful Nvidia constellation here. Do
you
He also asked for you to call
him and so I I'm sure you know his
numbers. Everybody call him right away.
Tell him that you think it's a great
idea. So this is going to be Nvidia
Constellation. We're going to build it.
We're going to start building as soon as
we can. We need the office space. Nvidia
Constellation Beetho Sheiling. Very
exciting.
Okay. Well, I want to thank all of you.
I want to thank all of you for your
partnership over the years. We are at a
once-in-a-lifetime opportunity. It is
not it is not an understatement to say
that the opportunity ahead of us is
extraordinary. For the very first time
in all of our time together, not only
are we
creating the next generation of IT,
we've done that several times from PC to
internet to cloud to mobile cloud. We've
done that several times. But this time,
not only are we creating the next
generation of IT, we are in fact
creating a whole new industry. This
whole new
industry is going to expose us to giant
opportunities ahead. I look forward to
partnering with all of you on building
AI factories, agents for enterprises,
robots, all of you amazing partners,
building the ecosystem with us around
one architecture. And so I want to thank
all of you for coming today. Have a
great Computex everybody.
Shash. Thank you.
Thank you for coming. Thank
[Music]
you. Heat. Heat.
[Music]