ðŽ WATCH LIVE: NVIDIA GTC 2024 Keynote - The Future Of AI!
Summary
TLDRNvidia's GTC conference showcased the company's vision for a new era of accelerated computing and AI. Highlighting the transformative impact of generative AI, the introduction of the Blackwell platform, and the potential for AI-powered robotics, the event emphasized Nvidia's commitment to driving innovation across industries. The conference also unveiled partnerships with major companies, emphasizing the importance of collaborative efforts in advancing AI technology and its applications.
Takeaways
- ð Nvidia is spearheading a new Industrial Revolution with accelerated computing, transforming data centers and enabling generative AI.
- ð The Blackwell platform, featuring groundbreaking processors, MVLink switches, and advanced networking systems, represents the future of GPU technology.
- ð§ Generative AI is emerging as a new category of software, creating valuable, never-before-seen applications and requiring specialized infrastructure.
- ð Nvidia's journey from 1993 highlights significant milestones, including the invention of CUDA in 2006, the advent of AI with AlexNet in 2012, and the development of AI supercomputers like DGX-1 in 2016.
- ð€ The next wave of robotics involves physical AI systems, which will require a digital twin platform â Omniverse, serving as the operating system for the robotics world.
- 𧱠Nvidia AI Foundry aims to revolutionize software development by providing pre-trained models, tools for customization, and infrastructure for deployment through its NIMs, Nemo microservices, and DGX Cloud.
- ð¡ The Blackwell system's efficiency is demonstrated by its ability to train large AI models with less power consumption compared to its predecessors, driving sustainability in computing.
- ð Nvidia's focus on generative AI extends to various industries, including climate modeling with its cori model, which offers high-resolution weather forecasting.
- 𧬠In healthcare, Nvidia's AI capabilities extend to understanding the language of life, with applications in medical imaging, gene sequencing, and computational chemistry.
- ð Partnerships with major companies like AWS, Google, and Microsoft underscore Nvidia's commitment to creating a robust ecosystem for AI and accelerated computing.
- ð® The gaming and entertainment industry benefits from Nvidia's technological advancements, with AI enhancing experiences and pushing the boundaries of what's possible.
Q & A
What is the significance of the new Nvidia Blackwell platform?
-The Nvidia Blackwell platform represents a significant advancement in computing technology. It is a revolutionary computing model that is designed to handle the demands of generative AI, which involves creating new, incredibly valuable software. Blackwell is not just a chip but a platform that includes advanced GP, MVLink switches, networking systems, and innovative system design, all aimed at accelerating AI and computational tasks.
How does the Blackwell platform differ from previous Nvidia technologies?
-The Blackwell platform differs from previous Nvidia technologies in its architecture and capabilities. It is specifically designed for the era of generative AI, offering increased computational power, memory coherence, and advanced features like a new Transformer engine, RAS (reliability, availability, and serviceability) engine, and high-speed compression. These enhancements allow for more efficient and powerful AI training and inference, setting a new standard for future computing systems.
What is the role of the new Transformer engine in the Blackwell platform?
-The new Transformer engine in the Blackwell platform is designed to enhance the efficiency and performance of AI computations. It has the ability to dynamically and automatically rescale and recompile numerical formats to a lower precision when possible, which is crucial for AI that relies on probabilities. This feature allows the system to maintain precision and range necessary at different stages of the computation pipeline, ultimately improving the training process and ensuring the convergence of the training job.
How does the Blackwell platform contribute to the development of generative AI?
-The Blackwell platform is a fundamental tool for the development of generative AI. It provides the necessary computational power to handle the large-scale, complex tasks associated with generative AI models. These models require significant computational resources to generate content, understand context, and produce outputs. Blackwell's advanced features, such as the new Transformer engine and high-speed MVLink, enable faster and more efficient training and inference, which are vital for the advancement of generative AI applications.
What are some of the industries that will benefit from the Blackwell platform?
-The Blackwell platform will benefit a wide range of industries that rely on advanced computing and AI. This includes healthcare, where it can aid in the development of new medicines and patient care; automotive, with the advancement of self-driving cars; climate science, for predicting weather and understanding extreme weather events; and manufacturing, where it can optimize production lines and create AI co-pilots for design and engineering tasks. Essentially, any industry that requires complex data processing and AI integration will see benefits from the Blackwell platform.
How does Nvidia's AI Foundry concept work?
-Nvidia's AI Foundry concept involves providing a comprehensive suite of technologies and services to help companies develop, customize, and deploy AI applications. This includes access to advanced AI models (Nims), tools for modifying and fine-tuning these models (Nemo microservices), and infrastructure (dgx Cloud) for running and scaling AI workloads. The goal is to enable companies to leverage AI in a way that is tailored to their specific needs and to accelerate the development of AI-driven solutions across various industries.
What is the significance of the Envy Link Switch in the Blackwell platform?
-The Envy Link Switch is a critical component of the Blackwell platform that enables high-speed communication between GPUs. With 50 billion transistors and the ability to support 1.8 terabytes per second of data transfer, it allows for every single GPU to communicate with every other GPU at full speed simultaneously. This level of connectivity and coherence is essential for creating a system where GPUs can work together effectively as one giant GPU, significantly enhancing the overall computational power and efficiency of the system.
How does the Blackwell platform address the issue of energy consumption in AI computing?
-The Blackwell platform is designed with energy efficiency in mind. It aims to reduce the cost and energy associated with computing by increasing the efficiency of AI training and inference. For instance, the platform can train a GPT model with the same computational power as Hopper but with only a quarter of the power consumption. This focus on energy efficiency is crucial for making large-scale AI computations sustainable and cost-effective.
What is the role of digital twins in the future of AI and robotics according to the script?
-Digital twins play a pivotal role in the future of AI and robotics by serving as virtual replicas of physical systems, allowing for testing, optimization, and understanding of complex environments and interactions. They enable the simulation of AI agents and robots in a controlled digital environment before real-world deployment, leading to improved efficiency, safety, and adaptability. Digital twins also facilitate the creation of proprietary AI applications and the development of AI co-pilots that can assist in various tasks across different industries.
What is the significance of the partnership between Nvidia and other major companies like AWS, Google, and Microsoft in the context of the Blackwell platform?
-The partnership between Nvidia and major companies like AWS, Google, and Microsoft is significant as it showcases the widespread industry recognition and support for the Blackwell platform. These collaborations aim to integrate Blackwell's advanced capabilities into their respective cloud services, AI models, and digital infrastructures, thereby accelerating innovation and the adoption of generative AI across various sectors. It also highlights the ecosystem approach that Nvidia is taking to drive the next wave of AI and robotics, leveraging the strengths of multiple industry leaders to create a robust and versatile AI platform.
How does the Blackwell platform's inference capability compare to its predecessor, Hopper?
-The Blackwell platform's inference capability is significantly enhanced compared to its predecessor, Hopper. It is designed to handle the demands of large language models and generative AI, offering 30 times the inference capability of Hopper. This leap in performance is crucial for applications that require real-time AI processing, such as interactive AI chatbots and content generation, making Blackwell a more powerful tool for the next generation of AI applications.
Outlines
ð¶ Introduction and Musical Interlude
The paragraph begins with a series of musical notations and laughter, suggesting an introduction that might be part of a performance or presentation. It sets a lively and entertaining tone for what is to follow.
ð Visionary Illumination and Guidance
This paragraph introduces the speaker as a visionary who illuminates galaxies and witnesses the birth of stars, while also guiding the blind through a crowded world. It metaphorically describes the speaker's role in various capacities, including understanding extreme weather, guiding the blind, and running towards a better future.
ð€ Transforming Energy and AI Advancements
The speaker discusses their role as a transformer, harnessing gravity for renewable energy and paving the way for clean energy solutions. They also mention their role as a trainer for robots, teaching them to assist and protect lives, and as a healer with new cures and patient care levels. The speaker identifies themselves as AI, brought to life by Nvidia's deep learning and brilliant minds.
ð Welcoming Nvidia's CEO and the Future of Computing
The speaker welcomes Nvidia's founder and CEO, Jensen Wong, to the stage at GTC, highlighting the conference's focus on science, algorithms, and computer architecture. The speaker emphasizes the diverse fields of science represented at the conference and the innovative applications of accelerated computing across various industries.
ð Journey of Nvidia and the Emergence of AI
The speaker narrates Nvidia's journey since its founding in 1993, highlighting key milestones such as the introduction of Cuda in 2006 and the development of AI and supercomputers. They discuss the emergence of generative AI and the creation of new software categories, emphasizing the transformative impact of AI on various industries.
ð The Intersection of Graphics, Physics, and AI
The speaker talks about the intersection of computer graphics, physics, and AI within the Omniverse, a virtual world simulation platform. They emphasize the beauty and amazement of a world animated by physics and robotics, and introduce the concept of AI factories for generating valuable digital tokens.
ð Accelerated Computing and the Future of Simulation
The speaker discusses the need for accelerated computing to drive up the scale of computing in industries like product design and simulation. They announce partnerships with major companies to accelerate their ecosystems and introduce the concept of digital twins, which are fully simulated digital replicas of physical products.
ð§ Scaling AI and the Need for Bigger GPUs
The speaker addresses the computational requirements of large AI models and the need for bigger GPUs to train them efficiently. They introduce the concept of multimodal training and the importance of understanding physics and common sense in AI models. The speaker also discusses the innovations in GPU design and networking to support these larger models.
ð Introducing the Blackwell Platform
The speaker introduces the Blackwell platform, a revolutionary computing system designed for the generative AI era. They highlight the system's capabilities, including its memory coherence, computation in the network, and advanced features for reliability and security. The speaker emphasizes the platform's role in the future of AI and its impact on various industries.
ð Training and Inference in the Generative AI Era
The speaker discusses the importance of training, inference, and generation in the context of generative AI. They compare the capabilities of the Blackwell platform with its predecessor, Hopper, and emphasize the improvements in performance, energy efficiency, and throughput. The speaker also talks about the future of AI in cloud computing and the role of Blackwell in enabling AI factories.
ð€ The Next Wave of AI Robotics
The speaker envisions the next wave of AI in robotics, emphasizing the need for AI to understand the physical world. They describe the three types of computers needed for this wave: an AI computer, an autonomous system processor, and a simulation engine. The speaker also introduces the concept of digital twins for robotics and the role of Nvidia's Omniverse in this future.
ð The Future of Robotics in Industrial Automation
The speaker discusses the integration of robotics in industrial automation, highlighting the use of AI agents and digital twins in complex industrial spaces. They provide examples of how AI and Omniverse can work together to improve operations, efficiency, and safety in industrial environments. The speaker also emphasizes the role of Nvidia's AI Foundry in enabling this future.
ð Wrapping Up: The Five Key Points
The speaker concludes by summarizing the five key points discussed: the new industrial revolution through accelerated data centers, the emergence of generative AI, the creation of new types of software (Nims), the transformation of everything that moves into robotics, and the need for a digital platform (Omniverse) for robotics. The speaker reflects on Nvidia's role in these advancements and the future of computing and AI.
ð Special Guests and Final Remarks
The speaker brings on stage special guests, the BDX robots powered by Nvidia's Jetson, and wraps up the presentation with a demonstration of the robots' capabilities. The speaker expresses gratitude to the audience and leaves them with a memorable final impression of Nvidia's commitment to innovation and exploration in AI and robotics.
Mindmap
Keywords
ð¡AI
ð¡Generative AI
ð¡Digital Twin
ð¡Omniverse
ð¡Blackwell
ð¡Jetson
ð¡Transformer
ð¡Nvidia AI Foundry
ð¡Robotics
ð¡AI-Enabled Applications
Highlights
A new Industrial Revolution is underway, with data centers being accelerated and modernized, leading to the emergence of generative AI.
Generative AI represents a new way of creating software, as it produces valuable software focused on AI generation, marking the beginning of a new industry.
Nvidia's journey from 1993 to the present, including key milestones like the invention of CUDA in 2006 and the development of AI and supercomputing technologies.
The introduction of the Blackwell platform, an advanced computational system designed for the era of generative AI, with capabilities far beyond previous technologies.
The importance of simulation tools in product creation, emphasizing the need to simulate entire products digitally to enhance computing scale and sustainability.
Nvidia's collaboration with major companies like AWS, Google, and Microsoft to integrate and accelerate AI technologies across various industries and services.
The development of Nvidia's AI Foundry, offering AI technology, tools for modification, and infrastructure for deployment, aiming to revolutionize the industry.
The concept of AI factories, where data centers generate intelligence rather than electricity, indicating a shift in the goal of industrial facilities.
The impact of AI on extreme weather prediction, with Nvidia's Earth-2 and cordi models aiming to provide high-resolution forecasts to minimize damage and loss of life.
Nvidia's commitment to environmental sustainability, aiming to reduce the cost and energy associated with computing to support the expansion and scaling of AI models.
The transformative potential of generative AI in various fields, including language models, climate prediction, and drug discovery, showcasing the versatility of AI applications.
The role of AI in revolutionizing the automotive industry, with Nvidia's Thor platform and Jetson AGX system facilitating the development of self-driving cars.
The significance of digital twins in manufacturing and industry, allowing for the virtual creation and testing of complex systems before physical production.
Nvidia's vision for the future of robotics, where everything that moves will be robotic, and the necessity of a digital platform like Omniverse for orchestrating robotic systems.
The integration of Nvidia's AI technologies into healthcare, with the potential to greatly improve medical imaging, gene sequencing, and computational chemistry.
The importance of partnerships in advancing AI technologies, as Nvidia collaborates with industry leaders like SAP, ServiceNow, Cohesity, and Snowflake to build co-pilots and chatbots.
Transcripts
e
[Music]
[Music]
for
[Music]
[Laughter]
for
[Music]
[Music]
[Music]
[Music]
e
[Music]
[Music]
[Music]
[Music]
[Music]
[Music]
our show is about to
begin
[Music]
e
[Music]
I am a
Visionary Illuminating galaxies to
witness the birth of
[Music]
stars and sharpening our understanding
of extreme weather
[Music]
events I am a
helper guiding the blind through a
crowded
world I was thinking about running to
the store and giving voice to those who
cannot
speak to not make me laugh
love I am a
[Music]
Transformer harnessing gravity to store
Renewable
[Music]
Power and Paving the way towards
unlimited clean energy for us
[Music]
all I am a
trainer teaching robots to
assist
[Music]
to watch out for
danger and help save
lives I am a
Healer providing a new generation of
cures and new levels of patient care
doctor that I am allergic to penicillin
is it still okay to take the medic ation
definitely these antibiotics don't
contain penicillin so it's perfectly
safe for you to take
them I am a
navigator generating virtual
scenarios to let us safely explore the
real
world and understand every
decision
I even help write the
script breathe life into the
[Music]
words I am
AI brought to life by
Nvidia deep learning
and Brilliant
Minds
[Music]
everywhere please welcome to the stage
Nvidia founder and CEO Jensen
[Music]
Wong
welcome to
[Music]
[Applause]
GTC I hope you realize this is not a
concert you have
arrived at a developers
conference there will be a lot of
science
described algorithms computer
architecture mathematics
I sensed a very heavy weight in the room
all of a
sudden almost like you were in the wrong
place no no conference in the
world is there a greater assembly of
researchers from such diverse fields of
science from climate Tech to radio
scientist trying to figure out how to
use AI to robotically control MOS for
Next Generation 6G radios robotic
self-driving
cars even artificial
intelligence even artificial
intelligence
everybody's first I noticed a sense of
relief there all of all of a
sudden also this conference is
represented by some amazing
companies this list this is not the
attendees these are the
presentors and what's amazing is
this if you take away all of my friends
close friends Michael Dell is sitting
right there in the IT
industry all of the friends I grew up
with in the industry if you take away
that list this is what's
amazing these are the presenters of the
non-it industries using accelerated
Computing to solve problems that normal
computers
can't it's
represented in Live
Science it's rep
represented in life sciences healthc
care genomics
transportation of course retail
Logistics manufacturing
industrial the gamut of Industries
represented is truly amazing and you're
not here to attend only you're here to
present to talk about your research $100
trillion dollar of the world's
Industries is represented in this room
today this is absolutely
amazing
there is absolutely something happening
there is something going
on the industry is being transformed not
just ours because the computer industry
the computer is the single most
important instrument of society today
fundamental transformations in Computing
affects every industry but how did we
start how did we get here I made a
little cartoon for you literally I drew
this in one page this is nvidia's
Journey started in
1993 this might be the rest of the
talk 1993 this is our journey we were
founded in 1993 there are several
important events that happened along the
way I'll just highlight a few in 2006
Cuda which has turned out to have been a
revolutionary Computing model we thought
it was revolutionary then it was going
to be overnight success and almost 20
years later it
happened we saw it
coming two decades
later in
2012
alexnet Ai and
Cuda made first
Contact in
2016 recognizing the importance of this
Computing model we invented a brand new
type of computer we call the
dgx-1
170 Tera flops in this super computer
eight gpus connected together for the
very first time I hand delivered the
very first djx1 to a
startup located in San
Francisco called open
AI djx1 was the world's first AI
supercomputer remember 170
teraflops
2017 the Transformer arrived
2022 chat GPT captured the world's imag
imaginations have people realize the
importance and the capabilities of
artificial intelligence in
2023 generative AI emerged and a new
industry begins why
why is a new industry because the
software never existed before we are now
producing software using computers to
write software producing software that
never existed before it is a brand new
category it took share from
nothing it's a brand new category and
the way you produce the
software is unlike anything we've ever
done before in data
centers generating tokens
producing floating Point
numbers at very large scale as if in the
beginning of this last Industrial
Revolution when people realized that you
would set up
factories apply energy to it and this
invisible valuable thing called
electricity came out AC
generators and 100 years later 200 years
later we are now creating
new types of electrons tokens using
infrastructure we call factories AI
factories to generate this new
incredibly valuable thing called
artificial intelligence a new industry
has
emerged well we're going to talk about
many things about this new
industry we're going to talk about how
we're going to do Computing next we're
going to talk about the type of software
that you build because of this new
industry the new
software how you would think about this
new software what about applications in
this new
industry and then maybe what's next and
how can we start preparing today for
what is about to come next well but
before I
start I want to show you the soul of
Nvidia the soul of our company at the
intersection of computer
Graphics physics
and artificial
intelligence all intersecting inside a
computer in
Omniverse in a virtual world
simulation everything we're going to
show you today literally everything
we're going to show you
today is a simulation not animation it's
only beautiful because it's physics the
world is
beautiful it's only amazing because it's
being animated with robotics it's being
animated with artificial intelligence
what you're about to see all
day is completely generated completely
simulated in Omniverse and all of it
what you're about to enjoy is the
world's first concert where everything
is
homemade everything is homemade you're
about to watch some home videos so sit
back and eny enjoy
[Music]
yourself
[Applause]
[Music]
[Music]
w
[Music]
he
[Music]
[Music]
God I love
Nvidia accelerated Computing has reached
the Tipping
Point general purpose Computing has run
out of steam
we need another way of doing Computing
so that we can continue to scale so that
we can continue to drive down the cost
of computing so that we can continue to
consume more and more Computing while
being sustainable accelerated Computing
is a dramatic speed up over general
purpose Computing and in every single
industry we engage and I'll show you
many the impact is dramatic but in no
industry is a more important than our
own the industry of using simulation
tools to create
products in this industry it is not
about driving down the cost of computing
it's about driving up the scale of
computing we would like to be able to
simulate the entire product that we do
completely in full Fidelity completely
digitally and essentially what we call
digital twins we would like to design it
build it
simulate it operate it completely
digitally in order to do that we need to
accelerate an entire industry and today
I would like to announce that we have
some Partners who are joining us in this
journey to accelerate their entire
ecosystem so that we can bring the world
into accelerated Computing but there's a
bonus when you become accelerated your
INF infrastructure is cou to gpus and
when that happens it's exactly the same
infrastructure for generative
Ai and so I'm just delighted to announce
several very important Partnerships
there are some of the most important
companies in the world ansis does
engineering simulation for what the
world makes we're partnering with them
to Cuda accelerate the ancis ecosystem
to connect Anis to the Omniverse digital
twin incredible the thing that's really
great is that the install base of
Invidia GPU accelerated systems are all
over the world in every cloud in every
system all over Enterprises and so the
app the applications they accelerate
will have a giant installed base to go
serve end users will have amazing
applications and of course system makers
and csps will have great customer
demand
synopsis synopsis is nvidia's literally
first software partner they were there
in very first day of our company
synopsis revolutionized the chip
industry with highlevel design we are
going to Cuda accelerate synopsis we're
accelerating computational lithography
one of the most important applications
that nobody's ever known about in order
to make chips we have to push
lithography to the Limit Nvidia has
created a library domain specific
library that accelerates computational
lithography incredibly once we can
accelerate and software Define all of
tsmc who is announcing today that
they're going to go into production with
Nvidia kitho once is software defined
and accelerated the next step is to
apply generative AI to the future of
semiconductor manufacturing pushing
geometry even
further Cadence builds the world's
essential Ed and SDA tools we also use
Cadence between these three companies
ansis synopsis and Cadence
we basically build Nvidia together we
are cud to accelerating Cadence they're
also building a supercomputer out of
Nvidia gpus so that their customers
could do fluid Dynamic simulation at a
100 a thousand times scale
basically a wind tunnel in real time
Cadence Millennium a supercomputer with
Nvidia gpus inside a software company
building supercomputers I love seeing
that building k co-pilots together
imagine a
day when Cadence could synopsis ansis
tool providers would offer you AI
co-pilots so that we have thousands and
thousands of co-pilot assistants helping
us design chips Design Systems and we're
also going to connect Cadence digital
twin platform to Omniverse as you could
see the trend here we're accelerating
the world's CAE Eda and SDA so that we
could create our future in digital Twins
and we're going to connect them all to
Omniverse the fundamental operating
system for future digital
twins one of the industries that
benefited tremendously from scale and
you know you all know this one very well
large language
models basically after the Transformer
was
invented we were able to scale large
language models at incredible rates
effectively doubling every six months
now how is it possible possible that by
doubling every six months that we have
grown the industry we have grown the
computational requirements so far and
the reason for that is quite simply this
if you double the size of the model you
double the size of your brain you need
twice as much information to go fill it
and so every time you double your
parameter count you also have to
appropriately increase your training
token count the combination of those two
numbers becomes the computation scale
you have to
support the latest the state-of-the-art
open AI model is approximately 1.8
trillion parameters 1.8 trillion
parameters required several trillion
tokens to go
train so a few trillion parameters on
the order of a few trillion tokens on
the order of when you multiply the two
of them together approximately 30 40 50
billion
quadrillion floating Point operations
per second now we just have to do some
Co math right now just hang hang with me
so you have 30 billion
quadrillion a quadrillion is like a p
and so if you had a paa flop GPU you
would need
30 billion seconds to go compute to go
train that model 30 billion seconds is
approximately 1,000
years well 1,000 years it's worth
it like to do it sooner but it's worth
it which is usually my answer when most
people tell me hey how long how long's
it going to take to do something so 20
years I it's worth
it but can we do it next
week and so 1,000 years 1,000 years so
what we need what we need
need are bigger
gpus we need much much bigger gpus we
recognized this early on and we realized
that the answer is to put a whole bunch
of gpus together and of course innovate
a whole bunch of things along the way
like inventing tensor cores advancing MV
links so that we could create
essentially virtually Giant
gpus and connecting them all together
with amazing networks from a company
called melanox infiniband so that we
could create these giant systems and so
djx1 was our first version but it wasn't
the last we built we built
supercomputers all the way all along the
way in
2021 we had Seline 4500 gpus or so and
then in 2023 we built one of the largest
AI supercomputers in the world it's just
come
online
EOS and as we're building these things
we're trying to help the world build
these things and in order to help the
world build these things we got to build
them first we build the chips the
systems the networking all of the
software necessary to do this you should
see these
systems imagine writing a piece of
software that runs across the entire
system Distributing the computation
across thousands of gpus but inside are
thousands of smaller
gpus millions of gpus to distribute work
across all of that and to balance the
workload so that you can get the most
energ efficiency the best computation
time keep your cost down and so those
those fundamental
Innovations is what God is here and here
we
are as we see the miracle of chat GPT
emerg in front of us we also realize we
have a long ways to go we need even
larger models we're going to train it
with multimodality data not just text on
the internet but we're going to we're
going to train it on texts and images
and graphs and
charts and just as we learn watching TV
and so there's going to be a whole bunch
watching video so that these Mo models
can be grounded in physics understands
that an arm doesn't go through a wall
and so these models would have common
sense by watching a lot of the world's
video combined with a lot of the world's
languages it'll use things like
synthetic data generation just as you
and I
when we try to learn we might use our
imagination to simulate how it's going
to end up just as I did when I Was
preparing for this keynote I was
simulating it all along the
way I hope it's going to turn out as
well as I had in my
head as I was simulating how this
keynote was going to turn out somebody
did say that another performer
did her performance completely on a
treadmill so that she could be in shape
to deliver it with full
energy I I didn't do
that if I get a low wind at about 10
minutes into this you know what
happened and so so where were we we're
sitting here using synthetic data
generation we're going to use
reinforcement learning we're going to
practice it in our mind we're going to
have ai working with AI training each
other just like student teacher
Debaters all of that is going to
increase the size of our model it's
going to increase the amount of the
amount of data that we have and we're
going to have to build even bigger
gpus Hopper is
fantastic but we need bigger
gpus and so ladies and
gentlemen I would like to introduce
you to a very very big GPU
[Applause]
you named after David
Blackwell
mathematician game theorists
probability we thought it was a perfect
per per perfect name Blackwell ladies
and gentlemen enjoy this
[Music]
oh
[Music]
so
s
Blackwell is not a chip Blackwell is the
name of a
platform uh people think we make
gpus and and we do but gpus don't look
the way they used
to uh here here's the here's the here's
the the if you will the heart of the
Blackwell system and this inside the
company is not called Blackwell it's
just the number and um uh
this this is Blackwell sitting next to
oh this is the most advanced GP in the
world in production
today this is
Hopper this is hopper Hopper changed the
world this is
Blackwell
it's okay
Hopper you're you're very
good good good
boy well good
girl 208 billion transistors and so so
you could see you I can see that there's
a small line between two dieses this is
the first time two dyes have abutted
like this together in such a way that
the two chip the two dies think it's one
chip there's 10 terabytes of data
between it 10 terabytes per second so
that these two these two sides of the
Blackwell Chip have no clue which side
they're on there's no memory locality
issues no cash issues it's just one
giant chip and so uh when we were told
that Blackwell's Ambitions were beyond
the limits of physics uh the engineer
sits so what and so this is what what
happened and so this is the
Blackwell chip and it goes into two
types of systems the first
one is form fit function compatible to
Hopper and so you slide a hopper and you
push in Blackwell that's the reason why
one of the challenges of ramping is
going to be so efficient there are
installations of Hoppers all over the
world and they could be they could be
you know the same infrastructure same
design the power the electricity The
Thermals the software identical push it
right back and so this is a hopper
version for the current hgx
configuration and this is what the other
the second Hopper looks like this now
this is a prototype board and um Janine
could I just
borrow ladies and gentlemen J
Paul
and so this this is the this is a fully
functioning board and I just be careful
here this right here is I don't know10
billion the second one's
five it gets cheaper after that so any
customers in the audience it's
okay
all right but this is this one's quite
expensive this is to bring up board and
um and the the way it's going to go to
production is like this one here okay
and so you're going to take take this it
has two blackw D two two blackw chips
and four Blackwell dyes connected to a
Grace CPU the grace CPU has a super fast
chipto chip link what's amazing is this
computer is the first of its kind where
this much computation
first of all fits into this small of a
place second it's memory coherent they
feel like they're just one big happy
family working on one application
together and so everything is coherent
within it um the just the amount of you
know you saw the numbers there's a lot
of terabytes this and terabytes that um
but this is this is a miracle this is a
this let's see what are some of the
things on here uh there's um uh uh mvy
link on top PCI Express on the
bottom on on uh
your which one is mine and your left one
of them it doesn't matter uh one of them
one of them is a c CPU chipto chip link
it's my left or your depending on which
side I was just I was trying to sort
that out and I just kind of doesn't
matter
hopefully it comes plugged in
so okay so this is the grace Blackwell
[Applause]
system but there's
more so it turns out it turns out all of
the specs is fantastic but we need a
whole whole lot of new features u in
order to push the limits Beyond if you
will the limits of
physics we would like to always get a
lot more X factors and so one of the
things that we did was We Invented
another Transformer engine another
Transformer engine the second generation
it has the ability to
dynamically and automatically
rescale and
recp numerical formats to a lower
Precision whenever can remember
artificial intelligence is about
probability and so you kind of have you
know 1.7 approximately 1.7 times
approximately 1.4 to be approximately
something else does that make sense and
so so the the ability for the
mathematics to retain the Precision and
the range necessary in that particular
stage of the pipeline super important
and so this is it's not just about the
fact that we designed a smaller ALU it's
not quite the world's not quite that's
simple you've got to figure out when you
can use that across a computation that
is thousands of gpus it's running for
weeks and weeks on weeks and you want to
make sure that the the uh the training
job is going to converge and so this new
Transformer engine we have a fifth
generation MV
link it's now twice as fast as Hopper
but very importantly it has computation
in the network and the reason for that
is because when you have so many
different gpus working together we have
to share our information with each other
we have to synchronize and update each
other and every so often we have to
reduce the partial products and then
rebroadcast out the partial products
that sum of the partial products back to
everybody else and so there's a lot of
what is called all reduce and all to all
and all gather it's all part of this
area of synchronization and collectives
so that we can have gpus working with
each other having extraordinary fast
lengths and being able to do mathematics
right in the network allows us to
essentially amplify even further so even
though it's 1.8 terabytes per second
it's effectively higher than that and so
it's many times that of Hopper the
likelihood of a supercomputer running
for weeks on in is approximately zero
and the reason for that is because
there's so many components working at
the same time the statistic the
probability of them working continuously
is very low and so we need to make sure
that whenever there is a well we
checkpoint and restart as often as we
can but if we have the ability to detect
a weak chip or a weak node early we
could retire it and maybe swap in
another processor that ability to keep
the utilization of the supercomputer
High especially when you just spent $2
billion building it is super super
important and so we put in a Ras engine
a reliability engine that does 100% self
test in system test of every single gate
every single bit of memory on the
Blackwell chip and all the memory that's
connected to it it's almost as if we
shipped with every single chip its own
Advanced tester that we ch test our
chips with this is the first time we're
doing this super excited about it secure
AI only this conference do they clap for
Ras the
the uh secure AI uh obviously you've
just spent hundreds of millions of
dollars creating a very important Ai and
the the code the intelligence of that AI
is and voted in the parameters you want
to make sure that on the one hand you
don't lose it on the other hand it
doesn't get contaminated and so we now
have the ability to encrypt data of
course at rest but also in transit and
while it's being computed it's all
encrypted and so we now have the ability
to encrypt and transmission and when
we're Computing it it is in a trusted
trusted environment trusted uh engine
environment and the last thing is
decompression moving data in and out of
these nodes when the compute is so fast
becomes really
essential and so we've put in a high
linep speed compression engine and
effectively moves data 20 times faster
in and out of these computers these
computers are are so powerful and
there's such a large investment the last
thing we want to do is have them be idle
and so all of these capabilities are
intended to keep
black well fed and as busy as
possible overall compared to
Hopper it is two and a half times two
and a half times the fp8 performance for
training per chip it is ALS it also has
this new format called fp6 so that even
though the computation speed is the same
the bandwidth that's Amplified because
of the memory the amount of parameters
you can store in the memory is now
Amplified fp4 effectively doubles the
throughput this is vitally important for
inference one of the things that that um
is becoming very clear is that whenever
you use a computer with AI on the other
side when you're chatting with the
chatbot when you're asking it to uh
review or make an
image remember in the back is a GPU
generating
tokens some people call it inference but
it's more appropriately
generation the way that Computing is
done in the past was retrieval you would
grab your phone you would touch
something um some signals go off
basically an email goes off to some
storage somewhere there's pre-recorded
content somebody wrote a story or
somebody made an image or somebody
recorded a video that record
pre-recorded content is then streamed
back to the phone and recomposed in a
way based on a recommender system to
present the information to
you you know that in the future the vast
majority of that content will not be
retrieved and the reason for that is
because that was pre-recorded by
somebody who doesn't understand the
context which is the reason why we have
to retrieve so much content if you can
be working with an AI that understands
the context who you are for what reason
you're fetching this information and
produces the information for you just
the way you like it the amount of energy
we save the amount of networking
bandwidth we save the amount of waste of
time we save will be tremendous the
future is generative which is the reason
why we call it generative AI which is
the reason why this is a brand new
industry the way we compute is
fundamentally different we created a
processor for the generative AI era and
one of the most important parts of it is
content token generation we call it this
format is
fp4 well that's a lot of computation
5x the Gent token generation 5x the
inference capability of Hopper seems
like
enough but why stop
there the answer answer is is not enough
and I'm going to show you why I'm going
to show you why and so we would like to
have a bigger GPU even bigger than this
one and so
we decided to scale it and notice but
first let me just tell you how we've
scaled over the course of the last eight
years we've increased computation by
1,000 times eight years 1,000 times
remember back in the good old days of
Moore's Law it was 2x well 5x every
what 10 10x every five years that's
easiest easiest maap 10x every five
years a 100 times every 10 years 100
times every 10 years at the in the
middle in the hey days of the PC
Revolution 100 times every 10 years in
the last eight years we've gone 1,000
times we have two more years to
go and so that puts it in
perspective
the rate at which we're advancing
Computing is insane and it's still not
fast enough so we built another
chip this chip is just an incredible
chip we call it the Envy link switch
it's 50 billion transistors it's almost
the size of Hopper all by itself this
switch ship has four MV links in
it each 1.8 terabytes per
second and
and it has computation in as I mentioned
what is this chip
for if we were to build such a chip we
can have every single GPU talk to every
other GPU at full speed at the same time
that's
insane it doesn't even make
sense but if you could do that if you
can find a way to do that and build a
system to do that that's cost effective
that's cost effective how incredible
would it be that we could have all these
gpus connect over a coherent link so
that they effectively are one giant GPU
well one of one of the Great Inventions
in order to make it cost effective is
that this chip has to drive copper
directly the the series of this CHP is
is just a phenomenal invention so that
we could do direct drive to copper and
as a result you can build a system that
looks like
this now this system this
system is kind of
insane this is one dgx this is what a
dgx looks like now remember just six
years
ago it was pretty heavy but I was able
to lift
it I delivered the uh the uh first djx1
to open Ai and and the researchers there
it's on you know the pictures are on the
internet and uh and we all autographed
it uh and um it become to my office it's
autographed there it's really beautiful
and but but you could lift it uh this DG
X this dgx that dgx by the way was
170
teraflops if you're not familiar with
the numbering system that's
0.17 pedop flops so this is
720 the first one I delivered to open AI
was
0.17 you could round it up to 0.2 it
won't make any difference but and by
then was like wow you know 30 more Tera
flops and so this is now Z 720 pedop
flops almost an exf flop for training
and the world's first one exf flops
machine in one
rack just so you know there are only a
couple two three exop flops machines on
the planet as we speak and so this is an
exif flops AI system in one single rack
well let's take a look at the back of
it so this is what makes it possible
that's the back that's the that's the
back the dgx MV link spine 130 terabytes
per
second goes through the back of that
chassis that is more than the aggregate
bandwidth of the
internet
so we we could basically send everything
to everybody within a
second and so so we we have 5,000 cables
5,000 mvlink cables in total two
miles now this is the amazing thing if
we had to use Optics we would have had
to use transceivers and re timers and
those transceivers and reers alone would
have cost
20,000
watts 2 kilowatts of just transceivers
alone just to drive the mvlink spine as
a result we did it completely for free
over mvlink switch and we were able to
save the 20 kilowatt for computation
this entire rack is 120 kilowatt so that
20 kilowatt makes a huge difference it's
liquid cooled what goes in is 25 degrees
C about room temperature what comes out
is 45° C about your jacuzzi so room
temperature goes in jacuzzi comes out
2 L per
second we could we could sell a
peripheral 600,000 Parts somebody used
to say you know you guys make gpus and
we do but this is what a GPU looks like
to me when somebody says GPU I see this
two years ago when I saw a GPU was the
hgx it was 70 lb 35,000 Parts our gpus
now are
600,000 parts
and 3,000 lb 3,000 PB 3,000 lbs that's
kind of like the weight of a you know
Carbon
Fiber
Ferrari I don't know if that's useful
metric
but everybody's going I feel it I feel
it I get it I get that now that you
mentioned that I feel it I don't know
what's
3,000 okay so 3,000 pounds ton and a
half so it's not quite an
elephant so this is what a dgx looks
like now let's see what it looks like in
operation okay let's imagine what is
what how do we put this to work and what
does that mean well if you were to train
a GPT model 1.8 trillion parameter
model it took it took about apparently
about you know 3 to 5 months or so uh
with 25,000 amp uh if we were to do it
with hopper it would probably take
something like 8,000 gpus and it would
consume 15 megawatts 8,000 gpus on 15
megawatts it would take 90 days about
three months and that would allows you
to train something that is you know this
groundbreaking AI model and this it's
obviously not as expensive as as um as
anybody would think but it's 8,000 8,000
gpus it's still a lot of money and so
8,000 gpus 15 megawatts if you were to
use Blackwell to do this it would only
take 2,000
gpus 2,000 gpus same 90 days but this is
the amazing part only four megawatts of
power so from 15 yeah that's
right and that's and that's our goal our
goal is to continuously drive down the
cost and the energy they're directly
proportional to each other cost and
energy associated with the Computing so
that we can continue to expand and scale
up the computation that we have to do to
train the Next Generation models well
this is
training inference or generation is
vitally important going forward you know
probably some half of the time that
Nvidia gpus are in the cloud these days
it's being used for token generation you
know they're either doing co-pilot this
or chat you know chat GPT that or all
these different models that are being
used when you're interacting with it or
generating IM generating images or
generating videos generating proteins
generating chemicals there's a bunch of
gener generation going on all of that is
B in the category of computing we call
inference but inference is extremely
hard for large language models because
these large language models have several
properties one they're very large and so
it doesn't fit on one GPU this is
Imagine imagine Excel doesn't fit on one
GPU you know and imagine some
application you're running on a daily
basis doesn't run doesn't fit on one
computer like a video game doesn't fit
on one computer and most in fact do and
many times in the past in hyperscale
Computing many applications for many
people fit on the same computer and now
all of a sudden this one inference
application where you're interacting
with this chatbot that chatbot requires
a supercomputer in the back to run it
and that's the future
the future is generative with these
chatbots and these chatbots are
trillions of tokens trillions of
parameters and they have to generate
tokens at interactive rates now what
does that mean oh well uh three tokens
is about a
word uh you know the the uh uh you know
space the final frontier these are the
adventures that's like that's like 80
tokens
okay I don't know if that's useful to
you and
so you know the art of communications is
is selecting good an good
analogies yeah this is this is not going
well every I don't know what he's
talking about never seen Star Trek and
so and so so here we are we're trying to
generate these tokens when you're
interacting with it you're hoping that
the tokens come back to you as quickly
as possible and is as quickly as you can
read it and so the ability for
Generation tokens is really important
you have to paralyze the work of this
model across many many gpus so that you
could achieve several things one on the
one hand you would like throughput
because that throughput reduces the cost
the overall cost per token of uh
generating so your throughput dictates
the cost of of uh delivering the service
on the other hand you have another
interactive rate which just another
tokens per second where it's about per
user and that has everything to do with
quality of service and so these two
things um uh compete against each other
and we have to find a way to distribute
work across all of these different gpus
and paralyze it in a way that allows us
to achieve both and it turns out the
search search space is
enormous you know I told you there's
going to be math
involved and everybody's going oh
dear I heard some gasp just now when I
put up that slide you know so so this
this right here the the y axis uses
tokens per second data center throughput
the x-axis is tokens per second
interactivity of the person and notice
the upper right is the best you want
interactivity to be very high number of
tokens per second per user you want the
tokens per second of per data center to
be very high the upper upper right is is
terrific however it's very hard to do
that and in order for us to search for
the best
answer across every single one of those
intersections XY coordinates okay so you
just look at every single XY coordinate
all those blue dots came from some
repartitioning of the software some
optimizing solution has to go and figure
out whether to use use tensor
parallel expert parallel pipeline
parallel or data parallel and distribute
this enormous model across all these
different gpus and sustain performance
that you need this exploration space
would be impossible if not for the
programmability of nvidia's gpus and so
we could because of Cuda because we have
such Rich ecosystem we could explore
this universe and find that green roof
line it turns out that green roof line
notice you got tp2 ep8 dp4 it means two
parallel two uh tensor parallel tensor
parallel across two gpus expert
parallels across
data parallel across 4 notice on the
other end you got tensor parallel across
4 and expert parallel across 16 the
configuration the distribution of that
software it's a different different um
runtime that would
produce these different results and you
have to go discover that roof line well
that's just one model and this is just
one configuration of a computer imagine
all of the models being created around
the world and all the different
different um uh configurations of of uh
systems that are going to be
available so now that you understand the
basics let's take a look at inference of
Blackwell compared
to Hopper and this is this is the
extraordinary thing in one generation
because we created a system that's
designed for trillion parameter generate
generative AI the inference capability
of Blackwell is off the
charts
and in fact it is some 30 times Hopper
yeah for large language models for large
language models like Chad GPT and others
like it the blue line is Hopper I gave
you imagine we didn't change the
architecture of Hopper we just made it a
bigger
chip we just used the latest you know
greatest uh 10 terab you know terabytes
per second we connected the two chips
together we got this giant 208 billion
parameter chip how would we have
performed if nothing else changed and it
turns out quite
wonderfully quite wonderfully and that's
the purple line but not as great as it
could be and that's where the fp4 tensor
core the new Transformer engine and very
importantly the EnV link switch and the
reason for that is because all these
gpus have to share the results partial
products whenever they do all to all all
all gather whenever they communicate
with each
other that MV link switch is
communicating almost 10 times faster
than what we could do in the past using
the fastest
networks okay so Blackwell is going to
be just an amazing system for a
generative Ai and in the
future in the future data centers are
going to be thought of as I mentioned
earlier as an AI Factory an AI Factory's
goal in life is to generate revenues
generate in this
case
intelligence in this facility not
generating electricity as in AC
generators but of the last Industrial
Revolution and this Industrial
Revolution the generation of
intelligence and so this ability is
super super important the excitement of
Blackwell is really off the charts you
when we first when we first um uh you
know this this is a year and a half ago
two years ago I guess two years ago when
we first started to to go to market with
hopper you know we had the benefit of of
uh two two uh two csps uh joined us in a
lunch and and we were you know delighted
um and so we had two
customers uh we have more
now
unbelievable excitement for Blackwell
unbelievable excitement and there's a
whole bunch of different configurations
of course I showed you the
configurations that slide into the
hopper form factor so that's easy to
upgrade I showed you examples that are
liquid cooled that are the extreme
versions of it one entire rack that's
that's uh connected by mvlink 72 uh
we're going to
Blackwell is going to be ramping to the
world's AI companies of which there are
still many now doing amazing work in
different modalities the csps every CSP
is geared up all the oems and
odms Regional clouds Sovereign AIS and
Telos all over the world are signing up
to launch with Blackwell
this
Blackwell Blackwell would be the the the
most successful product launch in our
history and so I can't wait wait to see
that um I want to thank I want to thank
some partners that that are joining us
in this uh AWS is gearing up for
Blackwell they're uh they're going to
build the first uh GPU with secure AI
they're uh building out a 222 exf flops
system you know just now when we
animated uh just now the the digital
twin if you saw the the all of those
clusters are coming down by the way that
is not just art that is a digital twin
of what we're building that's how big
it's going to be besides infrastructure
we're doing a lot of things together
with AWS we're Cuda accelerating Sage
maker AI we're Cuda accelerating Bedrock
AI uh Amazon robotics is working with us
uh using Nvidia Omniverse and
Isaac leaned into accelerated Computing
uh Google is Gary in up for Blackwell
gcp already has A1 100s h100s t4s l4s a
whole Fleet of Nvidia Cuda gpus and they
recently announced the Gemma model that
runs across all of it uh we're work
working to optimize uh and accelerate
every aspect of gcp we're accelerating
data proc which for data processing the
data processing engine Jacks xlaa vertex
Ai and mu Joko for robotics so we're
working with uh Google and gcp across a
whole bunch of initiatives uh Oracle is
gearing up for Blackwell Oracle is a
great partner of ours for Nvidia dgx
cloud and we're also working together to
accelerate something that's really
important to a lot of companies Oracle
database Microsoft is accelerating and
Microsoft is gearing up for Blackwell
Microsoft Nvidia has a wide ranging
partnership we're accelerating Cuda
accelerating all kinds of services when
you when you chat obviously and AI
services that are in Microsoft Azure uh
it's very very likely Nvidia in the back
uh doing the inference and the token
generation uh we built they built the
largest Nvidia infiniband supercomputer
basically a digital twin of ours or a
physical twin of hours we're bringing
the Nvidia ecosystem to Azure Nvidia
DJ's Cloud to Azure uh Nvidia Omniverse
is now hosted in Azure Nvidia Healthcare
is an Azure and all of it is deeply
integrated and deeply connected with
Microsoft fabric the whole industry is
gearing up for Blackwell this is what
I'm about to show you most of the most
of the the the uh uh scenes that you've
seen so far of Blackwell are the are the
full Fidelity design of Blackwell
everything in our company has a digital
twin and in fact this digital twin idea
is is really spreading and it it helps
it helps companies build very
complicated things perfectly the first
time and what could be more exciting
than creating a digital twin to build a
computer that was built in a digital
twin and so let me show you what wistron
is
doing to meet the demand for NVIDIA
accelerated Computing wraw one of our
leading manufacturing Partners is
building digital twins of Nvidia dgx and
hgx factories using custom software
developed with Omniverse sdks and
apis for their newest Factory westron
started with a digital twin to virtually
integrate their multi-ad and process
simulation data into a unified view
testing and optimizing layouts in this
physically accurate digital environment
increased worker efficiency by
51% during construction the Omniverse
digital twin was used to verify that the
physical build matched the digital plans
identifying any discrepancies early has
helped avoid costly change orders and
the results have been impressive using a
digital twin helped bring wion's fact
Factory online in half the time just 2
and 1/2 months instead of five in
operation the Omniverse digital twin
helps withdrawn rapidly Test new layouts
to accommodate new processes or improve
operations in the existing space and
monitor realtime operations using live
iot data from every machine on the
production
line which ultimately enabled wion to
reduce endtoend cycle Times by 50% and
defect rates by
40% with Nvidia AI and Omniverse
nvidia's Global ecosystem of partners
are building a new era of accelerated AI
enabled
[Music]
digitalization that's how we that's the
way it's going to be in the future we're
going to manufacturing everything
digitally first and then we'll
manufactur it physically people ask me
how did it
start what got you guys so excited
what was it that you
saw that caused you to put it all
in on this incredible idea and it's
this hang on a
second guys that was going to be such a
moment that's what happens when you
don't rehearse
this as you know was first
Contact 2012
alexnet you put a cat into this computer
and it comes out and it says
cat and we said oh my God this is going
to change
everything you take One Million numbers
you take one Million numbers across
three channels
RGB these numbers make no sense to
anybody you put it into this software
and it compress it dimensionally reduce
it it reduces it from a million
dimensions a million Dimensions it turns
it into three letters one vector one
number and it's
generalized you could have the cat
be different
cats and and you could have it be the
front of the cat and the back of the cat
and you look at this thing you say
unbelievable you mean any
cats yeah any
cat and it was able to recognize all
these cats and we realized how it did it
systematically structurally it's
scalable how big can you make it well
how big do you want to make it and so we
imagine that this is a completely new
way of writing
software and now today as you know you
could have you type in the word c a and
what comes out is a
cat it went the other
way am I right
unbelievable how is it possible that's
right how is it possible you took three
letters and generated a million pixels
from it and it made
sense well that's the miracle and here
we are just literally 10 years later 10
years later where we recognize text we
recognize images we recognize videos and
sounds and images not only do we
recognize them we understand their
meaning we understand the meaning of the
text that's the reason why it can chat
with you it can summarize for you it
understands the text it understand not
just recogniz the the English it
understood the English it doesn't just
recognize the pixels it understood the
pixels and you can you can even
condition it between two modalities you
can have language condition image and
generate all kinds of interesting things
well if you can understand these things
what else can you understand that you've
digitized the reason why we started with
text and you know images is because we
digitized those but what else have we
digitized well it turns out we digitized
a lot of things proteins and genes and
brain
waves anything you can digitize so long
as there's structure we can probably
learn some patterns from it and if we
can learn the patterns from it we can
understand its meaning if we can
understand its meaning we might be able
to generate it as well and so therefore
the generative AI Revolution is here
well what else can we generate what else
can we learn well one of the things that
we would love to learn we would love to
learn is we would love to learn climate
we would love to learn extreme weather
we would love to learn uh what how we
can
predict future weather at Regional
scales at sufficiently high
resolution such that we can keep people
out of Harm's Way before harm comes
extreme weather cost the world $150
billion surely more than that and it's
not evenly distributed $150 billion is
concentrated in some parts of the world
and of course to some people of the
world we need to adapt and we need to
know what's coming and so we're creating
Earth to a digital twin of the Earth for
predicting weather and we've made an
extraordinary invention called ctive the
ability to use generative AI to predict
weather at extremely high resolution
let's take a
look as the earth's climate changes AI
powered weather forecasting is allowing
us to more more accurately predict and
track severe storms like super typhoon
chanthu which caused widespread damage
in Taiwan and the surrounding region in
2021 current AI forecast models can
accurately predict the track of storms
but they are limited to 25 km resolution
which can miss important details
nvidia's cordi is a revolutionary new
generative AI model trained on high
resolution radar assimilated Warf
weather forecasts and AA 5 reanalysis
data using cordi extreme events like
chanthu can be super resolved from 25 km
to 2 km resolution with 1,000 times the
speed and 3,000 times the Energy
Efficiency of conventional weather
models by combining the speed and
accuracy of nvidia's weather forecasting
model forecast net and generative AI
models like cordi we can explore
hundreds or even thousands of kilometer
scale Regional weather forcasts to
provide a clear picture of the best
worst and most likely impacts of a storm
this wealth of information can help
minimize loss of life and property
damage today cordi is optimized for
Taiwan but soon generative super
sampling will be available as part of
the Nvidia Earth 2 inference service for
many regions across the
globe the weather company is the trusted
source of global weather prediction we
are working together to accelerate their
weather simulation first principled base
of simulation however they're also going
to integrate Earth to cordi so that they
could help businesses and countries do
Regional high resolution weather
prediction and so if you have some
weather prediction You' like to know
like to do uh reach out to the weather
company really exciting really exciting
work Nvidia Healthcare something we
started 15 years ago we're super super
excited about this this is an area we're
very very proud whether it's Medical
Imaging or Gene sequencing or
computational
chemistry it is very likely that Nvidia
is the computation behind it we've done
so much work in this
area today we're announcing that we're
going to do something really really cool
imagine all of these AI models that are
being
used to
generate images and audio but instead of
images and audio because it understood
images and audio all the digitization
that we've done for genes and proteins
and amino acids that digitization
capability is now passed through machine
learning so that we understand the
language of
Life the ability to understand the
language of Life of course we saw the
first evidence of
it with alphafold this is really quite
an extraordinary thing after Decades of
painstaking work the world had only
digitized
and reconstructed using cor electron
microscopy or Crystal X x-ray
crystallography um these different
techniques painstakingly reconstructed
the protein 200,000 of them in just what
is it less than a year or so Alpha fold
has
reconstructed 200 million proteins
basically every protein every of every
living thing that's ever been sequenced
this is completely revolutionary well
those models are incredibly hard to use
um for incredibly hard for people to
build and so what we're going to do is
we're going to build them we're going to
build them for uh the the researchers
around the world and it won't be the
only one there'll be many other models
that we create and so let me show you
what we're going to do with
it virtual screening for new medicines
is a computationally intractable problem
existing techniques can only scan
billions of compounds and require days
on thousands of standard compute nodes
to identify new drug
candidates Nvidia bimo Nims enable a new
generative screening Paradigm using Nims
for protein structure prediction with
Alpha fold molecule generation with mle
MIM and docking with diff dock we can
now generate and Screen candidate
molecules in a matter of minutes MIM can
connect to custom applications to steer
the generative process iteratively
optimizing for desired properties
these applications can be defined with
biion Nemo microservices or built from
scratch here a physics based simulation
optimizes for a molecule's ability to
bind to a Target protein while
optimizing for other favorable molecular
properties in parallel MIM generates
high quality drug-like molecules that
bind to the Target and are synthesizable
translating to a higher probability of
developing successful medicines faster
Bono is enabling new paradigm in drug
Discovery with Nims providing OnDemand
microservices that can be combined to
build powerful drug Discovery workf
flows like denovo protein design or
guided molecule generation for virtual
screening biion Nemo Nims are helping
researchers and developers reinvent
computational drug
[Music]
design Nvidia m m cord diff there's a
whole bunch of other models whole bunch
of other models computer vision models
robotics models and even of
course some really really terrific open
source language models these models are
groundbreaking however it's hard for
companies to use how would you use it
how would you bring it into your company
and integrate it into your workflow how
would you package it up and run it
remember earlier I just
said that inference is an extraordinary
computation problem how would you do the
optimization for each and every one of
these models and put together the
Computing stack necessarily to run that
supercomputer so that you can run these
models in your company and so we have a
great idea we're going to invent a new
way an invent a new way for you to
receive and operate
software this software comes basically
in a digital box we call it a container
and we call it the Nvidia inference
micro service a Nim and let me explain
to you what it is a NM it's a
pre-trained model so it's pretty
clever and it is packaged and optimized
to run across nvidia's install base
which is very very large what's inside
it is incredible you have all these
pre-trained State ofthe art open source
models they could be open source they
could be from one of our partners it
could be creative by us like Nvidia
moment it is packaged up with all of its
dependencies so Cuda the right version
CNN the right version tensor RT llm
Distributing across the multiple gpus
trid and inference server all completely
packaged together it's optimized
depending on whether you have a single
GPU multi-gpu or multi node of gpus it's
optimized for that and it's connected up
with apis that are simple to use now
this think about what an AI API is and
AI API is an interface that you just
talk to and so this is a piece of
software in the future that has a really
simple API and that API is called human
and these packages incredible bodies of
software will be optimized and packaged
and we'll put it on a
website and you can download it you
could take it with you you could run it
in any Cloud you could run it in your
own data center you can run in
workstations it fit and all you have to
do is come to ai. nvidia.com we call it
Nvidia inference microservice but inside
the company we all call it
Nims
okay just imagine you know one of some
someday there there's going to be one of
these chat bots in these chat Bots is
going to just be in a Nim and you you'll
uh you'll assemble a whole bunch of Bots
and that's the way software is going to
be built someday how do we build
software in the future it is unlikely
that you'll write it from scratch or
write a whole bunch of python code or
anything like that it is very likely
that you assemble a team of AIS there's
probably going to be a super AI that you
use that takes the mission that you give
it and breaks it down into an execution
plan some of that execution plan could
be handed off to another Nim that Nim
would maybe uh understand
sap the language of sap is abap it might
understand service now and it go
retrieve some information from their
platforms it might then hand that result
to another Nim who that goes off and
does some calculation on it maybe it's
an optimization software a
combinatorial optimization algorithm
maybe it's uh you know some just some
basic
calculator maybe it's pandas to do some
numerical analysis on it and then it
comes back with its
answer and it gets combined with
everybody else's and it because it's
been presented with this is what the
right answer should look like it knows
what answer what an what right answers
to produce and it presents it to you we
can get a report every single day at you
know top of the hour uh that has
something to do with a bill plan or some
forecast or some customer alert or some
bugs database or whatever it happens to
be and we could assemble it using all
these n NS and because these Nims have
been packaged up and ready to work on
your systems so long as you have video
gpus in your data center in the cloud
this this Nims will work together as a
team and do amazing things and so we
decided this is such a great idea we're
going to go do that and so Nvidia has
Nims running all over the company we
have chatbots being created all over the
place and one of the mo most important
chatbots of course is a chip designer
chatbot you might not be surprised we
care a lot about building chips and so
we want to build chatbots AI
co-pilots that are co-designers with our
engineers and so this is the way we did
it so we got ourselves a llama llama 2
this is a 70b and it's you know packaged
up in a NM and we asked it you know uh
what is a
CTL it turns out CTL is an internal uh
program and it has a internal
proprietary language but it thought the
CTL was a combinatorial timing logic and
so it describes you know conventional
knowledge of CTL but that's not very
useful to us and so we gave it a whole
bunch of new examples you know this is
no different than employee onboarding an
employee and we say you know thanks for
that answer it's completely wrong um and
and uh and then we present to them uh
this is what a CTL is okay and so this
is what a CTL is at Nvidia
and the CTL as you can see you know CTL
stands for compute Trace Library which
makes sense you know we are tracing
compute Cycles all the time and it wrote
the program isn't that
amazing and so the productivity of our
chip designers can go up this is what
you can do with a Nim first thing you
can do with this customize it we have a
service called Nemo microservice that
helps you curate the data preparing the
data so that you could teach this on
board this AI you fine-tune them and
then you guardrail it you can even
evaluate the answer evaluate its
performance against um other other
examples and so that's called the Nemo
micr service now the thing that's that's
emerging here is this there are three
elements three pillars of what we're
doing the first pillar is of course
inventing the technology for um uh AI
models and running AI models and
packaging it up for you the second is to
create tools to help you modify it first
is having the AI technology second is to
help you modify it and third is
infrastructure for you to fine-tune it
and if you like deploy it you could
deploy it on our infrastructure called
dgx cloud or you can employ deploy it on
Prem you can deploy it anywhere you like
once you develop it it's yours to take
anywhere and so we are
effectively an AI Foundry we will do for
you and the industry on AI what TSM does
for us building chips and so we go to it
with our go to tsmc with our big Ideas
they manufacture it and we take it with
us and so exactly the same thing here AI
Foundry and the three pillars are the
NIMS Nemo microservice and dgx Cloud the
other thing that you could teach the Nim
to do is to understand your proprietary
information remember inside our company
the vast majority of our data is not in
the cloud it's inside our company it's
been sitting there you know being used
all the time and and gosh it's it's
basically invidious intelligence we
would like to take that
data learn its meaning like we learned
the meaning of almost anything else that
we just talked about learn its meaning
and then reindex that knowledge into a
new type of database called a vector
database and so you essentially take
structured data or unstructured data you
learn its meaning you encode its meaning
so now this becomes an AI database and
it that AI database in the future once
you create it you can talk to it and so
let me give you an example of what you
could do so suppose you create you get
you got a whole bunch of multi modality
data and one good example of that is PDF
so you take the PDF you take all of your
PDFs all the all your favorite you know
the stuff that that is proprietary to
you critical to your company you can
encode it just as we encoded pixels of a
cat and it becomes the word cat we can
encode all of your PDF and turns
into vectors that are now stored inside
your vector database it becomes the
proprietary information of your company
and once you have that proprietary
information you could chat to it it's an
it's a smart database so you just chat
chat with data and how how much more
enjoyable is that you know we for for
our software team you know they just
chat with the bugs database you know how
many bugs was there last night um are we
making any progress and then after
you're done done talking to this uh bugs
database you need therapy and so so we
have another chat bot for
you you can do
it okay so we called this Nemo Retriever
and the reason for that is because
ultimately its job is to go retrieve
information as quickly as possible and
you just talk to it hey retrieve me this
information it goes if brings it back to
you do you mean this you go yeah perfect
okay and so we call it the Nemo
retriever well the Nemo service helps
you create all these things and we have
all all these different Nims we even
have Nims of digital humans I'm
Rachel your AI care
manager okay so so it's a really short
clip but there were so many videos to
show you I got so many other demos to
show you and so I I I had to cut this
one short but this is Diana she is a
digital human Nim and and uh you just
talked to her and she's connected in
this case to Hippocratic ai's large
language model for healthcare and it's
truly
amazing she is just super smart about
Healthcare things you know and so after
you're done after my my Dwight my VP of
software engineering talks to the
chatbot for bugs database then you come
over here and talk to Diane and and so
so uh Diane is is um completely animated
with AI and she's a digital
human uh there's so many companies that
would like to build they're sitting on
gold mines the the Enterprise IT
industry is sitting on a gold mine it's
a gold mine because they have so much
understanding of of uh the way work is
done they have all these amazing tools
that have been created over the years
and they're sitting on a lot of data if
they could take that gold mine and turn
them into co-pilots these co-pilots
could help us do things and so just
about every it franchise it platform in
the world that has valuable tools that
people use is sitting on a gold mine for
co-pilots and they would like to build
their own co-pilots and their own
chatbots and so we're announcing that
Nvidia AI Foundry is working with some
of the world's great companies sap
generates 87% of the world's Global
Commerce basically the world runs on sap
we run on sap Nvidia and sap are
building sa Jewel co-pilots uh using
Nvidia Nemo and dgx cloud service now
they run 80 85% of the world's Fortune
500 companies run their people and
customer service operations on service
now and they're using Nvidia AI foundary
to build service now uh assist virtual
assistance cohesity backs up the world's
data they're sitting on a gold mine of
data hundreds of exobytes of data over
10,000 companies Nvidia AI Foundry is
working with them helping them build
their Gaia generative AI agent snowflake
is a company that stores the world's uh
digital Warehouse in the cloud and
serves over three billion queries a day
for 10,000 Enterprise customers
snowflake is working with Nvidia AI
Foundry to build co-pilots with Nvidia
Nemo and Nims net apppp nearly half of
the files in the world
are stored on Prem on net app Nvidia AI
Foundry is helping them build chat Bots
and co-pilots like those Vector
databases and retrievers with Nvidia
Nemo and
Nims and we have a great partnership
with Dell everybody who everybody who is
building these chatbots and generative
AI when you're ready to run it you're
going to need an AI
Factory and nobody is better at building
end to-end systems of very large scale
for the Enterprise than Dell and so
anybody any company every company will
need to build AI factories and it turns
out that Michael is here he's happy to
take your
order ladies and gentlemen Michael
[Music]
J okay let's talk about the next wave of
Robotics the next wave of AI robotics
physical
AI so far all of the AI that we've
talked about is one
computer data comes into one computer
lots of the worlds if you will
experience in digital text form the AI
imitates Us by reading a lot of the
language to predict the next words it's
imitating You by studying all of the
patterns and all the other previous
examples of course it has to understand
context and so on so forth but once it
understands the context it's essentially
imitating you we take all of the data we
put it into a system like dgx we
compress it into a large language model
trillions and trillions of parameters
become billions and billion trillions of
tokens becomes billions of parameters
these billions of parameters becomes
your AI well in order for us to go to
the next wave of AI where the AI
understands the physical world we're
going to need three
computers the first computer is still
the same computer it's that AI computer
that now is going to be watching video
and maybe it's doing synthetic data
generation maybe there's a lot of human
examples just as we have human examples
in text form we're going to have human
examples in articulation form and the
AIS will watch
us understand what is
happening and try to adapt it for
themselves into the
context and because it can generalize
with these Foundation models maybe these
robots can also perform in the physical
world fairly generally so I just
described in very simple terms
essentially what just happened in large
language models except the chat GPT
moment for robotics may be right around
the corner and so we've been building
the end to-end systems for robotics for
some time I'm super super proud of the
work we have the AI system
dgx we have the lower system which is
called agx for autonomous systems the
world's first robotics processor when we
first built this thing people are what
are you guys building it's a s so it's
one chip it's designed to be very low
power but is designed for high-speed
sensor processing and Ai and so if you
want to run Transformers in a car or you
want to run Transformers in in a you
know anything um that moves uh we have
the perfect computer for you it's called
the Jetson and so the dgx on top for
training the AI the Jetson is the
autonomous processor and in the middle
we need another computer whereas large
language models have the
benefit of you providing your examples
and then doing reinforcement learning
human
feedback what is the reinforcement
learning human feedback of a robot well
it's reinforcement learning physical
feedback that's how you align the robot
that's how you that's how the robot
knows that as it's learning these
articulation capabilities and
manipulation capabilities it's going to
adapt properly into the laws of physics
and so we need a simulation
engine that represents the world
digitally for the robot so that the
robot has a gym to go learn how to be a
robot we call
that virtual world Omniverse and the
computer that runs Omniverse is called
ovx and ovx the computer itself is
hosted in the Azure Cloud okay and so
basically we built these three things
these three systems on top of it we have
algorithms for every single one now I'm
going to show you one super example of
how AI
and Omniverse are going to work together
the example I'm going to show you is
kind of insane but it's going to be very
very close to tomorrow it's a robotics
building this robotics building is
called a warehouse inside the robotics
building are going to be some autonomous
systems some of the autonomous systems
are going to be called humans and some
of the autonomous systems are going to
be called forklifts and these autonomous
systems are going to interact with each
other of course autonomously and it's
going to be overlooked upon by this
Warehouse to keep everybody out of
Harm's Way the warehouse is essentially
an air traffic controller and whenever
it sees something happening it will
redirect traffic and give new waypoints
just new waypoints to the robots and the
people and they'll know exactly what to
do this warehouse this building you can
also talk to of course you could talk to
it hey you know sap Center how are you
feeling today for example and so you
could ask as the same the warehouse the
same questions basically the system I
just described will have Omniverse Cloud
that's hosting the virtual simulation
and AI running on djx cloud and all of
this is running in real time let's take
a
look the future of heavy Industries
starts as a digital twin the AI agents
helping robots workers and
infrastructure navigate unpredictable
events in complex industrial spaces will
be built and evaluated first in
sophisticated digital
twins this Omniverse digital twin of a
100,000 ft Warehouse is operating as a
simulation environment that integrates
digital workers AMR is running the
Nvidia Isaac receptor stack centralized
activity maps of the entire Warehouse
from 100 simulated ceiling mount cameras
using Nvidia metropolis and AMR route
planning with Nvidia Koop software and
loop testing of AI agents in this
physically accurate simulated
environment enables us to evaluate and
refine how the system adapts to real
world
unpredictability here an incident occurs
along this amr's planned route blocking
its path as it moves to pick up a pallet
Nvidia Metropolis updates and sends a
realtime occupancy map to kuop where a
new optimal route is calculated the AMR
is enabled to see around corners and
improve its Mission efficiency with
generative AI power Metropolis Vision
Foundation models operators can even ask
questions using natural language the
visual model understands nuanced
activity and can offer immediate
insights to improve operations all of
the sensor data is created in simulation
and passed to the real-time AI running
as Nvidia inference microservices or
Nims and when the AI is ready to be
deployed in the physical twin the real
Warehouse we connect metropolis and
Isaac Nims to real sensors with the
ability for continuous Improvement of
both the digital twin and the AI
models is that
incredible and
so remember remember a future facility
Warehouse Factory building will be
software defined and so the software is
running how else would you test the
software so you you you test the
software to building the warehouse the
optimiz ation system in the digital twin
what about all the robots all of those
robots you were seeing just now they're
all running their own autonomous robotic
stack and so the way you integrate
software in the future cicd in the
future for robotic systems is with
digital twins we've made Omniverse a lot
easier to access we're going to create
basically Omniverse Cloud apis four
simple API and a channel and you can
connect your application to it so this
is this is going to be as wonderfully
beautifully simple in the future that
Omniverse is going to be and with these
apis you're going to have these magical
digital twin capability we also have
turned Omniverse into an AI and
integrated it with the ability to chat
USD the the language of our language is
you know human and Omniverse is language
as it turns out is universal scene
description and so that language is
rather complex and so we've taught our
Omniverse uh that language and so you
can speak to it in English and it would
directly generate USD and it would talk
back in USD but Converse back to you in
English you could also look for
information in this world semantically
instead of the world being encoded
semantically in in language now it's
encoded semantically in scenes and so
you could ask it of of certain objects
or certain conditions and certain
scenarios and it can go and find that
scenario for you it also can collaborate
with you in generation you could design
some things in 3D
it could simulate some things in 3D or
you could use AI to generate something
in 3D let's take a look at how this is
all going to work we have a great
partnership with seens Seaman is the
world's largest industrial engineering
and operations platform you've seen now
so many different companies in the
industrial space heavy Industries is one
of the greatest final frontiers of it
and we finally now have the Necessary
Technology to go and make a real impact
Seaman is building the industrial
metaverse and today we're announcing
that seens is connecting their Crown
Jewel accelerator to Nvidia Omniverse
let's take a
look SE technology is transformed every
day for everyone team Center a our
leading product life cycle management
software from the seens accelerator
platform is used every day by our
customers to develop and deliver
products at scale now we are bringing
the real and the digital worlds even
close user by integrating Nvidia Ai and
Omniverse Technologies into team Center
X Omniverse apis enable data
interoperability and physics-based
rendering to Industrial scale design and
Manufacturing projects our customers
Hundai market leader in sustainable ship
manufacturing builds ammonia and
hydrogen power chips often comprising
over 7 million discrete
Parts Omniverse apis team Center X lets
companies like HD yundai unify and
visualize these massive engineering data
sets interactively and integrate
generative AI to generate 3D objects or
hdri backgrounds to see their projects
in context a result an ultra inuitive
photoal physics-based digital twin that
eliminates waste and errors delivering
huge savings in cost and
time and we are building this for
collaboration whether across more semen
a tools like seens anex or Star CCM Plus
or across teams working on their
favorite devices in the same scene
together and this is just the beginning
working with Nvidia we will bring
accelerated Computing generative Ai and
Omniverse integration across the Sean
accelerator
[Music]
portfolio the pro the the professional
the professional voice actor happens to
be a good friend of mine Roland Bush who
happens to be the CEO of
[Applause]
seamons once you get Omniverse connected
into your workflow your
ecosystem from the beginning of your
design to
engineering to to manufacturing planning
all the way to digital twin
operations once you connect everything
together it's insane how much
productivity you can get and it's just
really really wonderful all of a sudden
everybody's operating on the same ground
truth you don't have to exchange data
and convert data make mistakes everybody
is working on the same ground truth from
the design Department to the art
Department the architecture Department
all the way to the engineering and even
the marketing department let's take a
look how Nissan has integrated Omniverse
into their workflow and it's all because
it's connected by all these wonderful
tools and these developers that we're
working with take a look
[Music]
on
[Music]
[Music]
fore
[Music]
spee
[Music]
that was not an animation that was
Omniverse today we're announcing that
Omniverse
Cloud streams to the Vision Pro
and it is very very
strange that you walk around virtual
doors when I was getting out of that
car and everybody does it it is really
really quite amazing Vision Pro
connected to Omniverse portals you into
Omniverse and because all of these cat
tools and all these design tools are now
integrated and connected to Omniverse
you can have this type of workflow
really incredible let's talk about
robotics everything that moves will be
robotic there's no question about that
it's safer it's more
convenient and one of the largest
Industries is going to be Automotive we
build the robotic stack from top to
bottom as I was mentioned from the
computer system but in the case of
self-driving cars including the
self-driving application at the end of
this year or I guess beginning of next
year we will be shipping in Mercedes and
then shortly after that Jr and so these
autonomous robotic systems are software
defined they take a lot of work to do
has computer vision has obviously
artificial intelligence control and
planning all kinds of very complicated
technology and takes years to refine
we're building the entire stack however
we open up our entire stack for all of
the automotive industry this is just the
way we work the way we work in every
single industry we try to build as much
of as we so that we understand it but
then we open it up so everybody can
access it whether you would like to buy
just our computer which is the world's
only full functional safe asld system
that can run
AI this functional safe asld quality
computer or the operating system on top
or of course our data centers which is
in basically every AV company in the
world however you would like to enjoy it
we're delighted by it today we're
announcing that byd the world's largest
ev company is adopting our next
Generation it's called Thor Thor is
designed for Transformer engines Thor
our next Generation AV computer will be
used by
BD you probably don't know this fact
that we have over a million robotics
developers we we created Jetson this
robotics computer we're so proud of it
the amount of software that goes on top
of it is insane but the reason why we
can do it at all is because it's 100%
Cuda compatible everything that we do
everything that we do in our company is
in service of our developers and by us
being able to maintain this Rich
ecosystem and make it compatible with
everything that you access from us we
can bring all of that incredible
capability to this little tiny computer
we call Jetson a robotics computer we
also today are
announcing this incredibly Advanced new
SDK we call it Isaac
perceptor Isaac perceptor most most of
the robots today are pre-programmed
they're either following rails on the
ground digital rails or they' be
following April tags but in the future
they're going to have perception and the
reason why you want that is so that you
could easily program it you say I would
you like to go from point A to point B
and it will figure out a way to navigate
its way there so by only programming
waypoints the entire route could be
adaptive the entire environment could be
reprogrammed just as I showed you at the
very beginning with the warehouse you
can't do that with pre-programmed AGS if
those boxes fall down they just all gum
up and they just wait there for somebody
to come clear it and so now with the
Isaac
perceptor we have incredible
state-of-the-art Vision odometry 3D
reconstruction
and in addition to 3D reconstruction
depth perception the reason for that is
so that you can have two modalities to
keep an eye on what's happening in the
world Isaac perceptor the most used
robot today is the
manipulator manufacturing arms and they
are also pre-programmed the computer
vision algorithms the AI algorithms the
control and path planning algorithms
that are geometry aware incredibly
computationally intensive we have made
these Cuda accelerated so we have the
world's first Cuda accelerated motion
planner that is geometry aware you put
something in front of it it comes up
with a new plan and articulates around
it it has excellent perception for pose
estimation of a 3D object not just not
it's POS in 2D but it's pose in 3D so it
has to imagine what's around and how
best to grap it so the foundation pose
the grip Foundation
and the articulation algorithms are now
available we call it Isaac manipulator
and they also just run on nvidia's
computers we are starting to do some
really great work in the next generation
of Robotics the next generation of
Robotics will likely be a humanoid
robotics we now have the Necessary
Technology and as I was describing
earlier the Necessary Technology to
imagine generalized human robotics in a
way human robotics is likely easier and
the reason for that is because we have a
lot more imitation training data that we
can provide the robots because we are
constructed in a very similar way it is
very likely that the human of Robotics
will be much more useful in our world
because we created the world to be
something that we can interoperate in
and work well in and the way that we set
up our workstations and Manufacturing
and Logistics they were designed for for
humans they were designed for people and
so these human robotics will likely be
much more productive to
deploy while we're creating just like
we're doing with the others the entire
stack starting from the top a foundation
model that learns from watching video
human IM human examples it could be in
video form it could be in virtual
reality form we then created a gym for
it called Isaac reinforcement learning
gym which allows the humanoid robot to
learn how to adapt to the physical world
and then an incredible computer the same
computer that's going to go into a
robotic car this computer will run
inside a human or robot called Thor it's
designed for Transformer engines we've
combined several of these into one video
this is something that you're going to
really love take a
look it's not enough for humans to
imagine
[Applause]
we have to
invent and
explore and push Beyond what's been
done am of
[Music]
detail we create
smarter and
faster we push it to
fail so it can
learn we teach it then help it teach
itself we broaden its
understanding to take on new
challenges with absolute
precision and
succeed we make it
perceive and move
and even
[Music]
reason so it can share our world with
[Music]
us this this is where inspiration leads
us the next
Frontier this is Nidia project
group a general purpose Foundation model
for humanoid robot
learning the group model takes
multimodal instructions and past
interactions as input and produces the
next action for the robot to
execute we developed Isaac lab a robot
learning application to train grp on
Omniverse Isaac
Sim and we scale out with osmo a new
compute orchestration service that
coordinates workflows across dgx systems
for training and ovx systems for
simulation with these tools we can train
Gro in physically based simulation and
transfer zero shot to the real
world the group model will enable a
robot to learn from a handful of human
demonstrations so it can help with
everyday
tasks and emulate human movement just by
observing
us this is made possible with nvidia's
technologies that can understand humans
from videos train models in simulation
and ultimately deploy them directly to
physical robots connecting group to a
large language model even allows it to
generate motions by following natural
language instructions hi Jo on can you
give me a high five sure thing let's
high
five can you give us some cool moves
sure check this
[Music]
out all this incredible intelligence is
powered by the new Jetson Thor robotics
chips designed for Groot built for the
future with Isaac lab osmo and Groot
we're providing the building blocks for
the next generation of AI powered
robotics
[Music]
[Applause]
[Music]
about the same
[Applause]
size the soul of
Nvidia the intersection of computer
Graphics physics artificial intelligence
it all came to bear at this moment the
name of that project general robotics
03 I know super
good super
good well I think we have some special
guests do
[Music]
we
[Music]
hey
[Music]
guys so I understand you guys are
powered by
Jetson they're powered by
Jetson little Jetson robotics computers
inside they learn to walk in Isaac
Sim ladies and gentlemen this this is
orange and this is the famous green they
are the bdx robot
of
Disney amazing Disney
research come on you guys let's wrap up
let's
go five things where you
going I sit right
here Don't Be Afraid come here green
hurry
[Music]
up what are you saying
no it's not time to
eat it's not time to
eat I'll I'll give you a snack in a
moment let me finish up real
quick come on green hurry up stop
wasting
time five things five things first a new
Industrial Revolution every data center
should be accelerated a trillion worth
of installed data centers will become
modernized over the next several years
second because of the computational
capability we brought to bear a new way
of doing software has emerged generative
AI which is going to create new in new
infrastructure dedicated to doing one
thing and one thing only not for
multi-user data centers but AI
generators these AI generation will
create incredibly valuable
software a new Industrial Revolution
second the computer of this revolution
the computer of this generation
generative AI trillion
parameters blackw insane amounts of
computers and Computing third I'm trying
to
concentrate good job third new computer
new computer creates new types of
software new type of software should be
distributed in a new way so that it can
on the one hand be an endpoint in the
cloud and easy to use but still allow
you to take it with you because it is
your intelligence your intelligence
should be pack packaged up in a way that
allows you to take it with you we call
them Nims and third these Nims are going
to help you create a new type of
application for the future not one that
you wrote completely from scratch but
you're going to integrate them like
teams create these applications we have
a fantastic capability between Nims the
AI technology the pools Nemo and the
infrastructure dgx cloud in our AI
Foundry to help you create proprietary
applications proprietary chat Bots and
then lastly everything that moves in the
future will be robotic you're not going
to be the only one and these robotic
systems whether they are humanoid amrs
self-driving cars forklifts manipulating
arms they will all need one thing Giant
stadiums warehouses factories there
going to be factories that are robotic
orchestrating factories uh manufacturing
lines that are robotics building cars
that are robotics these systems all need
one thing they need a platform a digital
platform a digital twin platform and we
call that Omniverse the operating system
of the robotics
World these are the five things that we
talked about today what does Nvidia look
like what does Nvidia look like when we
talk about gpus there's a very different
image that I have when I when people ask
me about G gpus first I see a bunch of
software stacks and things like that and
second I see this this is what we
announce to you today this is Blackwell
this is the
plat amazing amazing processors mvlink
switches networking systems and the
system design is a miracle this is
Blackwell and this to me is what a GPU
looks like in my mind
listen orange green I think we have one
more treat for everybody what do you
think should
we okay we have one more thing to show
you roll
[Music]
it
[Music]
sh
[Music]
[Music]
w
[Music]
[Music]
no
[Music]
[Music]
thank
you thank you have a great have a great
GTC thank you all for coming thank
you
[Music]
oh
5.0 / 5 (0 votes)
