🔴 WATCH LIVE: NVIDIA GTC 2024 Keynote - The Future Of AI!
Summary
TLDRNvidia 在 GTC 大会上展示了其在人工智能、高性能计算和机器人技术方面的最新进展。介绍了新的 Blackwell GPU 平台、先进的网络系统和系统设计，以及如何通过生成式 AI 创造新的价值和软件类型。此外，Nvidia 强调了 Omniverse 数字孪生平台在机器人技术中的应用，以及如何通过 AI 推动工业革命。
Takeaways
- 🚀 我们正处于一场新的工业革命的边缘，数据中心的现代化将推动这一变革。
- 🌟 计算能力的显著提升催生了生成式AI，这将创造新的软件类别和基础设施。
- 🤖 未来，所有移动的物体都将被机器人化，这包括自动驾驶汽车、叉车、机械臂等。
- 💡 英伟达(Nvidia)正在构建一个全面的机器人技术堆栈，从AI训练到物理世界中的实际操作。
- 🔌 英伟达推出了Jetson Thor，这是一款为机器人和自动驾驶汽车设计的下一代AI计算机。
- 🔄 Omniverse成为了机器人世界的操作系统，提供了一个数字孪生平台，用于模拟和训练AI。
- 🧠 英伟达的AI Foundry提供了Nims（Nvidia推理微服务），这是一种新的软件分发方式。
- 🔧 通过Nvidia的技术和工具，如Isaac Sim和Osmo，可以在物理基础上训练和模拟机器人。
- 🌐 英伟达与各行业领导者合作，将AI和Omniverse技术整合到他们的工作流程中，如与SEAM合作打造工业元宇宙。
- 🛠️ 英伟达的Blackwell GPU平台和MVLink开关展示了公司在加速计算和网络系统方面的最新成就。
- 🎉 英伟达正在推动AI和机器人技术的边界，为未来的创新和生产力提升奠定基础。
Q & A
Nvidia的创始人和CEO是谁？
-Nvidia的创始人和CEO是Jensen Huang。
Nvidia在1993年的旅程中有哪些重要的里程碑？
-Nvidia在1993年成立，2006年推出了革命性的计算模型Cuda，2012年AlexNet AI和Cuda首次接触，2016年推出了世界上第一台AI超级计算机dgx-1，2017年Transformer出现，2022年Chat GPT吸引了全世界的注意，2023年生成性AI出现，标志着一个新行业的开始。
Nvidia的Blackwell平台是什么？
-Blackwell不是单个芯片，而是一个平台，它包括了多个GPU和连接这些GPU的高速网络系统，以及专为AI计算设计的软件和系统架构。
Nvidia的Envy Link Switch芯片有什么特点？
-Envy Link Switch芯片拥有50亿个晶体管，几乎与Hopper芯片的大小相当。这个交换机内置了四个MV链接，每个链接的速度为1.8TB每秒，并且芯片内嵌入了计算能力。
Nvidia如何推动AI在不同行业的应用？
-Nvidia通过提供强大的计算平台和软件工具，如dgx超级计算机、Jetson机器人计算平台、Omniverse数字孪生平台等，以及与各行各业的合作伙伴共同开发针对特定需求的AI解决方案，从而推动AI在不同行业的应用。
Nvidia的AI工厂概念是什么？
-Nvidia的AI工厂概念是指将数据中心视为AI的生产工厂，其目标是生成智能而非传统的电力。这意味着AI工厂将专注于创建和运行大型AI模型，以生成内容、进行数据分析和支持决策制定等。
Nvidia的Blackwell平台与Hopper相比有哪些性能提升？
-与Hopper相比，Blackwell平台在训练性能上提升了2.5倍，支持的fp8性能每芯片提升，同时引入了新的fp6格式，使得尽管计算速度相同，但由于内存的增加，带宽得到放大。此外，fp4有效吞吐量翻倍，这对推理尤其重要。
Nvidia如何帮助合作伙伴加速其生态系统？
-Nvidia通过提供Cuda加速的软件工具、Omniverse数字孪生平台、以及强大的计算平台如dgx和Jetson，帮助合作伙伴加速其生态系统。此外，Nvidia还与合作伙伴共同开发针对特定行业的AI解决方案，以推动整个行业的数字化转型。
Nvidia的AI技术在医疗领域的应用有哪些？
-Nvidia的AI技术在医疗领域的应用包括医疗成像、基因测序、计算化学等。Nvidia的GPU是这些领域背后的关键计算力量，而且Nvidia还推出了专门针对医疗行业的AI模型和服务，如用于蛋白质结构预测的AlphaFold。
Nvidia的Jetson Thor是为哪种类型的机器人设计的？
-Jetson Thor是为下一代AI驱动的机器人设计的，特别是那些需要在物理世界中进行感知、操作和自主导航的机器人。
Outlines
🎶 音乐与笑声的序幕
视频脚本的开头以音乐和笑声为背景，营造出一种轻松愉快的氛围。
🌌 愿景与启示：星系的见证者
第二段介绍了演讲者作为一位富有远见的启示者，他通过音乐和星辰的比喻，描述了自己在理解极端天气事件、帮助盲人导航以及作为助手的角色。
🤖 转型与创新：力量的传递者
第三段中，演讲者变身为一个变革者，利用重力储存可再生能源，并引领我们走向无限清洁能源的未来。他还是一位训练师，教导机器人如何避免危险并拯救生命。
🎭 演绎与创造：AI的诞生
第四段描述了Nvidia如何通过深度学习和杰出人才，将AI带入生活。演讲者提到了Nvidia创始人兼CEO Jensen Huang的欢迎词，并强调了GTC会议的重要性。
🌐 数字孪生与行业变革
第五段强调了数字孪生技术在各个行业中的广泛应用，包括生命科学、医疗保健、交通和制造业等。演讲者提到了这些行业如何通过加速计算来解决传统计算机无法解决的问题。
🚀 计算的飞跃：模拟与创新
第六段讨论了计算的未来发展，包括如何通过模拟工具创建产品，以及数字孪生如何推动整个行业的加速。演讲者宣布了与重要公司合作，共同加速生态系统的建设。
🧠 人工智能的大脑：GPU的进化
第七段详细介绍了Nvidia GPU的发展历程，从早期的Cuda到最新的Blackwell平台。演讲者强调了Blackwell平台的创新之处，包括其对大型AI模型的推理能力。
🔄 网络与协同：MV链接的革新
第八段讲述了MV链接技术的进步，以及它如何使多个GPU之间的数据传输变得更加高效。演讲者提到了这种技术在构建大型AI超级计算机中的应用。
🛠️ 可靠性与安全性：AI的保障
第九段强调了Blackwell芯片的可靠性和安全性，包括其自我测试功能以及对AI的加密保护。演讲者提到了如何通过这些技术来确保AI的安全性和数据的保密性。
🚀 性能的飞跃：Blackwell的卓越
第十段中，演讲者详细介绍了Blackwell平台的性能优势，包括其在训练和推理方面的显著提升。他还提到了Blackwell如何通过新的计算格式和网络技术来提高效率。
🌟 未来展望：生成式AI的崛起
第十一段讨论了生成式AI的未来，以及它如何改变我们与计算机的互动方式。演讲者强调了生成式AI在内容创作和个性化体验中的潜力。
🔥 能源与效率：Blackwell的热效率
第十二段中，演讲者讨论了Blackwell平台在能源效率方面的突破，包括其在训练大型AI模型时的能耗降低。他还提到了Blackwell如何通过液冷技术来管理热量。
🌐 网络与连接：Blackwell的网络架构
第十三段强调了Blackwell平台的网络架构，包括其如何通过MV链接和EnV链接交换机来实现GPU之间的高速通信。演讲者提到了这种架构在提高计算效率方面的重要性。
🤖 机器人与AI：物理智能的未来
第十四段中，演讲者展望了机器人和AI在物理世界中的应用，包括自动驾驶汽车和工业自动化。他还提到了Nvidia如何通过其技术平台支持这些应用的开发。
🌟 合作与创新：Nvidia的合作伙伴
第十五段讨论了Nvidia与其合作伙伴共同推动AI技术发展的努力，包括与AWS、Google、Microsoft和Oracle等公司的合作。演讲者强调了这些合作如何加速AI技术的创新和应用。
🌍 气候与预测：AI在气候科学中的应用
第十六段中，演讲者介绍了Nvidia如何利用AI技术来预测天气和气候变化，以及这种技术如何帮助我们更好地理解和应对极端天气事件。
💊 医疗与发现：AI在药物研发中的应用
第十七段讨论了Nvidia如何利用AI技术来加速药物发现和医疗诊断的过程，包括通过生成式AI模型来预测蛋白质结构和分子对接。
🧠 智能服务：Nvidia的AI微服务
第十八段中，演讲者介绍了Nvidia的AI微服务（Nims），这是一种预训练的模型，可以优化并打包运行在Nvidia的硬件上。他还提到了如何通过Nems来创建和部署AI应用。
🤖 机器人的进化：Nvidia的机器人技术
第十九段讨论了Nvidia在机器人技术方面的进展，包括开发用于自动驾驶汽车的计算平台和用于机器人的Jetson芯片。演讲者还提到了Nvidia如何通过AI技术来提高机器人的感知和操作能力。
🌐 数字孪生与协作：Omniverse的应用
第二十段中，演讲者介绍了Nvidia的Omniverse平台，这是一个用于创建和模拟数字孪生的系统。他还提到了Omniverse如何与工业工程和操作平台Seamans合作，以及如何通过AI技术来提高工业生产效率。
🚀 机器人的未来：Nvidia的机器人战略
第二十一段中，演讲者展望了机器人技术的未来，包括开发人形机器人和自动化系统。他还提到了Nvidia如何通过其技术平台来支持这些机器人的开发和部署。
🎉 总结与展望：Nvidia的未来方向
最后一段总结了Nvidia在AI、机器人技术和数字孪生领域的主要成就，并展望了公司的未来方向。演讲者强调了Nvidia如何通过其技术平台来推动工业革命和创新。
Mindmap
Keywords
💡加速计算
💡生成式AI
💡Blackwell
💡数字孪生
💡Omniverse
💡Jetson
💡AI工厂
💡机器人技术
💡Nvidia AI Foundry
💡Isaac Sim
Highlights
Nvidia的旅程始于1993年，经历了多个重要里程碑，包括2006年的Cuda、2012年的AlexNet AI和Cuda的首次接触，以及2016年推出的世界上第一台AI超级计算机dgx-1。
2022年，Transformer的出现和Chat GPT的兴起使人们意识到人工智能的重要性和能力，预示着一个新产业的诞生。
Nvidia的Omniverse是一个虚拟世界仿真平台，它将计算机图形学、物理学和人工智能相结合，展示了公司的灵魂。
Nvidia宣布与多个重要公司合作，包括Ansys、Synopsys和Cadence，以加速其生态系统，并连接到Omniverse数字孪生。
Nvidia介绍了Blackwell平台，这是一个新的GPU平台，旨在推动生成性AI的发展，具有2080亿个晶体管和10TB/秒的数据传输速度。
Nvidia的Jetson AGX是一个为自主系统设计的低功耗、高速传感器处理和AI的处理器，适用于自动驾驶汽车等移动应用。
Nvidia的OVX计算机运行Omniverse，是构建数字孪生和模拟环境的基础，支持机器人学习和物理反馈。
Nvidia的AI Foundry为企业提供AI模型、工具和基础设施，帮助他们创建和部署定制的AI应用和服务。
Nvidia与Seeq合作，将其工业工程和运营平台与Omniverse集成，提高工业规模设计和制造项目的效率。
Nvidia的Isaac感知器SDK为机器人提供了先进的视觉里程计、3D重建和深度感知能力，使其能够适应环境并自主导航。
Nvidia的Thor是下一代自动驾驶汽车计算机，专为Transformer引擎设计，将被比亚迪采用。
Nvidia的Gro项目组是一个通用的人类机器人学习模型，能够在观看视频、模仿人类动作和执行日常任务方面进行自我学习。
Nvidia的Jetson Thor机器人芯片为未来的AI驱动机器人提供了强大的计算能力，包括Isaac实验室、Osmo和Gro。
Nvidia的数字孪生技术Omniverse Cloud API使得开发者可以轻松地将其应用程序与数字孪生能力连接起来。
Nvidia的AI工厂概念预示着未来的数据中心将被视为AI工厂，其目标是生成智能而非电力。
Nvidia的Nims（Nvidia推理微服务）是一种新的软件分发方式，允许用户以数字盒的形式下载和运行预训练模型。
Transcripts
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our show is about to
begin
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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
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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
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yourself
[Applause]
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w
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he
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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
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on
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fore
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spee
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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
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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
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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
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we
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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
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it
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sh
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w
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no
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thank
you thank you have a great have a great
GTC thank you all for coming thank
you
[Music]
oh
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