NVIDIA'S HUGE AI Chip Breakthroughs Change Everything (Supercut)

Summary

Huang unveils Nvidia's new Grace Hopper and H100 chips, representing a tipping point in accelerated computing and AI. Powering Nvidia's end-to-end AI platform, these chips enable breakthroughs in language models and vast improvements in performance and efficiency. Huang also announces partnerships and modular server designs to bring AI capabilities to the enterprise and every industry. Ultimately, he conveys a vision where AI empowers every company to become an 'intelligence producer', with AI factories fueled by Nvidia's accelerated computing platform.

Takeaways

• 😲 Nvidia has reached a tipping point in accelerated computing and generative AI.
• 👩‍💻 Software is now programmed by engineers working with AI supercomputers.
• 🔋 Accelerated computing is reinventing software from the ground up.
• 🚀 Nvidia GPU performance has increased 1,000x in 5 years.
• 🏭 AI supercomputers are a new type of factory for intelligence production.
• 😎 Grace Hopper, Nvidia's new accelerated computing chip, is now in full production.
• 🤯 The new DGX GH200 AI supercomputer delivers 1 exaflop of AI performance.
• 🌐 Every data center will be re-engineered for accelerated computing.
• 💼 Nvidia AI Enterprise makes accelerated computing enterprise-grade.
• 🔌 Nvidia is extending accelerated computing and AI from cloud to edge.

Q & A

What are the two fundamental transitions happening in the computer industry according to Huang?

The two fundamental transitions are: 1) CPU scaling has ended, so the ability to get 10x more performance every 5 years has ended. 2) A new way of doing software via deep learning and AI was discovered, reinventing computation from the ground up.

What is accelerated computing and how does it work?

Accelerated computing uses GPUs and other specialized hardware to dramatically speed up workloads like AI, data analytics, and graphics. It allows much higher performance and efficiency than general purpose CPUs.

What is the Grace Hopper superchip?

The Grace Hopper is Nvidia's new accelerated computing CPU chip for AI and high performance computing. It has nearly 200 billion transistors and high speed memory built in for efficiency.

What is the H100 and why is it important?

The H100 is Nvidia's latest AI accelerator GPU. With advanced features like the Transformer Engine, it delivers giant leaps in AI performance to power the next wave of generative AI.

What is the goal of the Nvidia MGX system?

The Nvidia MGX is a new open and modular server architecture optimized specifically for accelerated computing. It allows flexible configurations for AI and HPC workloads.

What does Huang mean when he says the computer is now the data center?

He means that with the rise of cloud computing and AI supercomputers, the focus has shifted from optimizing single servers to building integrated data centers with networks of servers acting as one giant computer.

What is Spectrum-X?

Spectrum-X is Nvidia's new high performance data center interconnect platform. It helps optimize AI supercomputing clusters using advanced networking and software.

How does Nvidia plan to bring accelerated computing to enterprises?

Nvidia aims to make accelerated computing enterprise-ready through solutions like the MGX reference architecture and the Nvidia AI Enterprise software stack for managing AI infrastructure securely.

What is the benefit of using accelerated computing in the cloud?

Accelerated computing in the cloud can provide up to 24x higher throughput and 20x lower cost compared to CPU-only platforms for workloads like image processing.

What role do Grace Hopper Superchips play in Nvidia's plan for AI supercomputing?

Nvidia will use Grace Hopper Superchips as the base components to build exascale AI supercomputers made up of 256 connected GH200 systems, delivering over 1 exaflop/s of AI performance.

Outlines

00:00

😊 Introducing the h100 AI Supercomputer

Jensen Huang introduces the h100 AI supercomputer, which has over 35,000 components and 8 Hopper GPUs. He shows the h100 system board and compute tray, calling it the world's most expensive computer at $200,000. He explains how the h100 replaces an entire room of computers and accelerates workloads significantly.

05:01

🚀 Accelerated Computing Has Reached a Tipping Point

Huang explains the two fundamental computing industry transitions happening: CPU scaling has ended, but deep learning allows giant leaps in AI every two years. He shows how accelerated computing with GPUs processes an AI workload much faster and cheaper than CPUs. Accelerated computing now has high utilization across clouds, data centers, and applications.

10:03

💡 AI Will Impact Every Industry

Huang discusses how easy access to large language models closes the digital divide and makes everyone a programmer. He explains why AI will enhance all existing applications, not just create new ones. The rate of progress will be fast since it's easy to use across industries.

15:04

🔥 Introducing Grace Hopper - The First Accelerated AI Supercomputer Processor

Huang unveils Grace Hopper, the world's first accelerated processor for AI with nearly 200 billion transistors and 600GB of coherent memory between the CPU and GPU. He shows how 256 Grace Hopper chips connected is an exascale system aimed at advancing generative AI.

20:08

📦 Announcing the NVIDIA MGX - A Modular Accelerated Computing Server Architecture

Huang introduces NVIDIA MGX, an open server architecture specification optimized for accelerated computing. It's modular and flexible to address diverse data center requirements across industry verticals and make AI accessible.

25:08

🌐 Spectrum-X - A New Interconnect to Make Any Data Center an AI Data Center

Huang proposes Spectrum-X, a high-throughput low-latency interconnect for supercomputing and AI workloads. Key capabilities are adaptive routing and congestion control to dramatically boost performance. This makes accelerated computing accessible across industries.

Keywords

💡Accelerated Computing

Accelerated computing refers to the use of specialized hardware like GPUs to dramatically speed up computing tasks like AI and deep learning. It is a core theme of the video, with the presenter explaining how they have focused on reinventing the GPU to handle tensor processing and AI workloads. He states that accelerated computing combined with AI represents a fundamental transition in the computing industry. Examples of accelerated computing in the script include Nvidia GPUs and the new Grace Hopper specialized AI chip.

💡Generative AI

Generative AI refers to AI systems that can generate new content like images, video, text etc. The presenter states that we have reached a tipping point for generative AI, enabled by progress in accelerated computing. He envisions generative AI being used to create 'AI factories' that can produce intelligence and content for companies. The Grace Hopper chips will help scale generative AI.

💡Grace Hopper

Grace Hopper is Nvidia's new specialized chip for accelerating AI and deep learning workloads. It has nearly 200 billion transistors and the presenter highlights advanced features like its giant coherent memory pool between the GPU and CPU. He announces they will connect 256 Grace Hopper chips into an 'exaflops transformer engine' called dgx gh200 aimed at pushing the boundaries of generative AI.

💡Nvidia AI

Nvidia AI refers to Nvidia's end-to-end software stack and tools that support deep learning and AI workloads. It works with their accelerated computing hardware like GPUs. The presenter explains how Nvidia AI helps take AI from data processing and training all the way to deployment and interference. It aims to increase ease of use and adoption of AI.

💡Hyperscale cloud

Hyperscale cloud refers to the massive cloud data centers run by companies like Amazon, Google and Microsoft that host websites and applications. The presenter contrasts the networking and architecture needs of hyperscale cloud with high performance computing uses like AI training that require tightly coupled workflows.

💡Spectrum-X

Spectrum-X is a new type of programmable ethernet platform announced by Nvidia to help optimize networking in AI and high performance computing applications. It introduces capabilities like adaptive routing and congestion control to dynamically manage traffic, helping accelerate jobs that require tight coordination between nodes.

💡Nvidia AI Enterprise

Nvidia AI Enterprise is a new software stack announced by Nvidia that packages all their AI libraries, tools and drivers into a single enterprise-grade solution. The presenter explains this will finally make accelerated computing and AI accessible for business and enterprise use cases that require security, manageability and support.

💡Modular architecture

The presenter emphasizes the need for modular and flexible server architecture to address the diverse accelerated computing use cases across industries. To that end, they announce the Nvidia mgx - an open standard for modular accelerated computing system designs that can be adapted for needs ranging from cloud graphics to AI training.

💡AI supercomputer

AI supercomputers refer to specialized computing systems optimized for AI workloads like training deep learning models. The presenter draws contrasts between the networking and architectural needs of AI supercomputers versus hyperscale cloud data centers. The dgx gh200 with 256 connected Grace Hopper chips is presented as a new AI supercomputer for pushing generative AI.

💡Exascale computing

Exascale computing refers to high performance computing systems capable of at least a billion billion operations per second. The presenter reveals their new dgx gh200 AI supercomputer with 256 Grace Hopper chips can achieve an exaflop in terms of transformer compute, allowing it to take on cutting edge generative AI workloads.

Highlights

Software is no longer programmed just by computer engineers, it's now programmed by engineers working with AI supercomputers.

We have reached the tipping point of accelerated computing and generative AI.

GPU servers are expensive, but the computer is now the data center. The goal is to build the most cost effective data center, not server.

This computing era can understand multi-modality, so it can impact every industry.

The programming barrier is low - everyone is a programmer now. You just have to say something to the computer.

This computer can improve every existing application with AI. New applications aren't needed for it to succeed.

Grace Hopper has nearly 200 billion transistors and almost 600GB of memory coherent between CPU and GPU.

Google Cloud, Meta, and Microsoft will pioneer AI research with the new DGX GH200 AI supercomputer.

The NVIDIA MGX is an open, modular server design for accelerated computing that's multi-generation and standardized.

NVIDIA AI Enterprise makes accelerated computing Enterprise grade and secure for the first time.

New adaptive routing and congestion control in switches will dramatically increase Ethernet performance for AI.

We're in a transition with accelerated computing and generative AI that's full stack, data center scale, and domain specific.

H100 is in full production as the engine of generative AI. Grace Hopper scales it out in data centers.

Spectrum-X with switches, cables and software extends generative AI from hyperscale to enterprise.

NVIDIA AI Enterprise brings accelerated computing to the cloud for every enterprise.

Transcripts

this is the new computer industry

software is no longer programmed just by

computer Engineers software is

programmed by computer Engineers working

with AI supercomputers we have now

reached the Tipping Point of accelerated

Computing we have now reached the

Tipping Point of generative Ai and we

are so so so excited to be in full

volume production of the h100 this is

going to touch literally every single

industry let's take a look at how h100

is produced

[Music]

okay

[Music]

35

000 components on that system board

eight

Hopper gpus

let me show it to you

all right this

I would I would lift this but I I um I

still have the rest of the keynote I

would like to give this is 60 pounds 65

pounds it takes robots to lift it of

course and it takes robots to insert it

because the insertion pressure is so

high and has to be so perfect

this computer is two hundred thousand

dollars and as you know it replaces an

entire room of other computers it's the

world's single most expensive computer

that you can say the more you buy the

more you save

this is what a compute trade looks like

even this is incredibly heavy

see that

so this is the brand new h100 with the

world's first computer that has a

Transformer engine in it

the performance is utterly incredible

there are two fundamental transitions

happening in the computer industry today

all of you are deep within it and you

feel it there are two fundamental Trends

the first trend is because CPU scaling

has ended the ability to get 10 times

more performance every five years has

ended the ability to get 10 times more

performance every five years at the same

cost is the reason why computers are so

fast today

that trend has ended it happened at

exactly the time when a new way of doing

software was discovered deep learning

these two events came together and is

driving Computing today

accelerated Computing and generative AI

of doing software just a way of doing

computation is a reinvention from the

ground up and it's not easy accelerated

Computing has taken us nearly three

decades to accomplish

well this is how accelerated Computing

works

this is accelerated Computing used for

large language models basically the core

of generative AI this example is a 10

million dollar server and so 10 million

dollars gets you nearly a thousand CPU

servers and to train to process this

large language model takes 11 gigawatt

hours 11 gigawatt hours okay and this is

what happens when you accelerate this

workload with accelerated Computing and

so with 10 million dollars for a 10

million dollar server you buy 48 GPU

servers it's the reason why people say

that GPU servers are so expensive

remember people say GPS servers are so

expensive however the GPU server is no

longer the computer the computer is the

data center

your goal is to build the most cost

effective data center not build the most

cost effective server

back in the old days when the computer

was the server that would be a

reasonable thing to do but today the

computer is the data center so for 10

million dollars you buy 48 GPU servers

it only consumes 3.2 gigawatt hours and

44 times the performance

let me just show it to you one more time

this is before and this is after and

this is

we want dense computers not big ones we

want dense computers fast computers not

big ones let me show you something else

this is my favorite

if your goal if your goal is to get the

work done

and this is the work you want to get

done ISO work

okay this is ISO work all right look at

this

look at this look at this before

after you've heard me talk about this

for so many years

in fact every single time you saw me

I've been talking to you about

accelerated computing

and now

why is it that finally it's the Tipping

Point because we have now addressed so

many different domains of science so

many Industries and in data processing

in deep learning classical machine

learning

so many different ways for us to deploy

software from the cloud to Enterprise to

Super Computing to the edge

so many different configurations of gpus

from our hgx versions to our Omniverse

versions to our Cloud GPU and Graphics

version so many different versions now

the utilization is incredibly High

the utilization of Nvidia GPU is so high

almost every single cloud is

overextended almost every single data

center is overextended there are so many

different applications using it so we

have now reached the Tipping Point of

accelerated Computing we have now

reached the Tipping Point of generative

AI

people thought that gpus would just be

gpus they were completely wrong we

dedicated ourselves to Reinventing the

GPU so that it's incredibly good at

tensor processing and then all of the

algorithms and engines that sit on top

of these computers we call Nvidia AI the

only AI operating system in the world

that takes data processing from data

processing to training to optimization

to deployment and inference

end to end deep learning processing it

is the engine of AI today

we connected gpus to other gpus called

mvlink build one giant GPU and we

connected those gpus together using

infiniband into larger scale computers

the ability for us to drive the

processor and extend the scale of

computing

made it possible

for the AI research organization the

community to advance AI at an incredible

rate

so every two years we take giant leaps

forward and I'm expecting the next lead

to be giant as well

this is the new computer industry

software is no longer programmed just by

computer Engineers software is

programmed by computer Engineers working

with AI supercomputers these AI

supercomputers

are a new type of factory

it is very logical that a car industry

has factories they build things so you

can see cars

it is very logical that computer

industry has computer factories you

build things that you can see computers

in the future

every single major company will also

have ai factories

and you will build and produce your

company's intelligence

and it's a very sensible thing

we are intelligence producers already

it's just that the intelligence

producers the intelligence are people in

the future we will be intelligence

producers artificial intelligence

producers and every single company will

have factories and the factories will be

built this way

using accelerated Computing and

artificial intelligence we accelerated

computer Graphics by 1 000 times in five

years

Moore's Law is probably currently

running at about two times

a thousand times in five years a

thousand times in five years is one

million times in ten we're doing the

same thing in artificial intelligence

now question is what can you do when

your computer is one million times

faster

what would you do if your computer was

one million times faster well it turns

out that we can now apply the instrument

of our industry to so many different

fields that were impossible before

this is the reason why everybody is so

excited

there's no question that we're in a new

Computing era

there's just absolutely no question

about it every single Computing era you

could do different things that weren't

possible before and artificial

intelligence certainly qualifies this

particular Computing era is special in

several ways one

it is able to understand information of

more than just text and numbers it can

Now understand multi-modality which is

the reason why this Computing Revolution

can impact every industry

every industry two

because this computer

doesn't care how you program it

it will try to understand what you mean

because it has this incredible large

language model capability and so the

programming barrier is incredibly low we

have closed the digital divide

everyone is a programmer now you just

have to say something to the computer

third

this computer

not only is it able to do amazing things

for the for the future

it can do amazing things for every

single application of the previous era

which is the reason why all of these

apis are being connected into Windows

applications here and there in browsers

and PowerPoint and word every

application that exists will be better

because of AI

you don't have to just AI this

generation this Computing era does not

need

new applications it can succeed with old

applications and it's going to have new

applications

the rate of progress the rate of

progress because it's so easy to use

is the reason why it's growing so fast

this is going to touch literally every

single industry and at the core with

just as with every single Computing era

it needs a new Computing approach

the last several years I've been talking

to you about the new type of processor

we've been creating

and this is the reason we've been

creating it

ladies and gentlemen

Grace Hopper is now in full production

this is Grace Hopper

nearly 200 billion transistors in this

computer oh

foreign

look at this this is Grace Hopper

this this processor

this processor is really quite amazing

there are several characteristics about

it this is the world's first accelerated

processor

accelerated Computing processor that

also has a giant memory it has almost

600 gigabytes of memory that's coherent

between the CPU and the GPU and so the

GPU can reference the memory the CPU can

represent reference the memory and

unnecessary any unnecessary copying back

and forth could be avoided

the amazing amount of high-speed memory

lets the GPU work on very very large

data sets this is a computer this is not

a chip practically the Entire Computer

is on here all of the Lo this is uh uses

low power DDR memory just like your cell

phone except this has been optimized and

designed for high resilience data center

applications so let me show you what

we're going to do so the first thing is

of course we have the Grace Hopper

Superchip

put that into a computer the second

thing that we're going to do is we're

going to connect eight of these together

using ndlink this is an Envy link switch

so eight of this eight of this Connect

into three switch trays into eight eight

Grace Hopper pod

these eight Grace Hopper pods each one

of the grace Hoppers are connected to

the other Grace Hopper at 900 gigabytes

per second

Aid them connected together

as a pod and then we connect 32 of them

together

with another layer of switches

and in order to build in order to build

this

256 Grace Hopper Super Chips connected

into one exoflops one exaflops you know

that countries and Nations have been

working on exaflops Computing and just

recently achieved it

256 Grace Hoppers for deep learning is

one exaflop Transformer engine and it

gives us

144 terabytes of memory that every GPU

can see

this is not 144 terabytes distributed

this is 144 terabytes connected

why don't we take a look at what it

really looks like play please

foreign

[Applause]

this

is

150 miles of cables

fiber optic cables

2 000 fans

70

000 cubic feet per minute

it probably

recycles the air in this entire room in

a couple of minutes

forty thousand pounds

four elephants

one GPU

if I can get up on here this is actual

size

so this is this is our brand new

Grace Hopper AI supercomputer it is one

giant GPU

utterly incredible we're building it now

and we're so we're so excited that

Google Cloud meta and Microsoft will be

the first companies in the world to have

access

and they will be doing

exploratory research on the pioneering

front the boundaries of artificial

intelligence with us so this is the dgx

gh200 it is one giant GPU

okay I just talked about how we are

going to extend the frontier of AI

data centers all over the world and all

of them over the next decade will be

recycled

and re-engineered into accelerated data

centers and generative AI capable data

centers but there are so many different

applications in so many different areas

scientific computing

data processing cloud and video and

Graphics generative AI for Enterprise

and of course the edge each one of these

applications have different

configurations of servers

different focus of applications

different deployment methods and so

security is different operating system

is different how it's managed it's

different

well this is just an enormous number of

configurations and so today we're

announcing in partnership with so many

companies here in Taiwan the Nvidia mgx

it's an open modular server design

specification and the design for

Accelerated Computing most of the

servers today are designed for general

purpose Computing the mechanical thermal

and electrical is insufficient for a

very highly dense Computing system

accelerated computers take as you know

many servers and compress it into one

you save a lot of money you save a lot

of floor space but the architecture is

different and we designed it so that

it's multi-generation standardized so

that once you make an investment our

next generation gpus and Next Generation

CPUs and next generation dpus will

continue to easily configure into it so

that we can best time to Market and best

preservation of our investment different

data centers have different requirements

and we've made this modular and flexible

so that it could address all of these

different domains now this is the basic

chassis let's take a look at some of the

other things you can do with it this is

the Omniverse ovx server

it has x86 four l40s Bluefield three two

CX-7 six PCI Express Lots this is the

grace Omniverse server

Grace same for l40s BF3 Bluefield 3 and

2 cx-7s okay this is the grace Cloud

Graphics server

this is the hopper NV link generative AI

inference server

and of course Grace Hopper liquid cooled

okay for very dense servers and then

this one is our dense general purpose

Grace Superchip server this is just CPU

and has the ability to accommodate four

CPU four gray CPUs or two gray

Superchips enormous amounts of

performance in ISO performance Grace

only consumes 580 Watts for the whole

for the whole server versus the latest

generation CPU servers x86 servers 1090

Watts it's basically half the power at

the same performance or another way of

saying

you know at the same power if your data

center is power constrained you get

twice the performance most data centers

today are power limited and so this is

really a terrific capability

we're going to expand AI into a new

territory

if you look at the world's data centers

the data center is now the computer and

the network defines what that data

center does largely there are two types

of data centers today there's the data

center that's used for hyperscale where

you have application workloads of all

different kinds the number of CPUs you

the number of gpus you connect to it is

relatively low the number of tenants is

very high the workloads are Loosely

coupled

and you have another type of data center

they're like super Computing data

centers AI supercomputers where the

workloads are tightly coupled

the number of tenants far fewer and

sometimes just one

its purpose is high throughput on very

large Computing problems

and so super Computing centers and Ai

supercomputers and the world's cloud

hyperscale cloud are very different in

nature

the ability for ethernet to interconnect

components of almost from anywhere is

the reason why the world's internet was

created if it required too much

coordination how could we have built

today's internet so ethernet's profound

contribution it's this lossy capability

is resilient capability and because so

it basically can connect almost anything

together

however a super Computing data center

can't afford that you can't interconnect

random things together because that

billion dollar supercomputer the

difference between 95 percent

networking throughput achieved versus 50

is effectively 500 million dollars

now it's really really important to

realize that in a high performance

Computing application every single GPU

must finish their job so that the

application can move on

in many cases where you do all

reductions you have to wait until the

results of every single one so if one

node takes too long everybody gets held

back

the question is how do we introduce

a new type of ethernet that's of course

backwards compatible with everything but

it's engineered in a way that achieves

the type of capabilities that we that we

can bring AI workloads to the world's

any data center first

adaptive routing adaptive routing

basically says based on the traffic that

is going through your data center

depending on which one of the ports of

that switch is over congested it will

tell Bluefield 3 to send and will send

it to another Port Bluefield 3 on the

other end would reassemble it and

present the data to the GPU without any

CPU intervention second congestion

control congestion control it is

possible for a certain different ports

to become heavily congested in which

case each switch will see how the

network is performing and communicate to

the senders please don't send any more

data right away

because you're congesting the network

that congestion control requires

basically a overriding system which

includes software the switch working

with all of the endpoints to overall

manage the congestion or the traffic and

the throughput of the data center this

capability is going to increase

ethernet's overall performance

dramatically

now one of the things that very few

people realize

is that today there's only one software

stack that is Enterprise secure and

Enterprise grade

that software stack is CPU

and the reason for that is because in

order to be Enterprise grade it has to

be Enterprise secure and has to be

Enterprise managed and Enterprise

supported over 4 000 software packages

is what it takes for people to use

accelerated Computing today in data

processing and training and optimization

all the way to inference so for the very

first time we are taking all of that

software

and we're going to maintain it and

manage it like red hat does for Linux

Nvidia AI Enterprise will do it for all

of nvidia's libraries now Enterprise can

finally have an Enterprise grade and

Enterprise secure software stack this is

such a big deal otherwise

even though the promise of accelerated

Computing is possible for many

researchers and scientists is not

available for Enterprise companies and

so let's take a look at the benefit for

them this is a simple image processing

application if you were to do it on a

CPU versus on a GPU running on

Enterprise Nvidia AI Enterprise you're

getting

31.8 images per minute or basically 24

times the throughput or you only pay

five percent of the cost

this is really quite amazing this is the

benefit of accelerated Computing in the

cloud but for many companies Enterprises

is simply not possible unless you have

this stack

Nvidia AI Enterprise is now fully

integrated into AWS Google cloud and

Microsoft Azure or an oracle Cloud it is

also integrated into the world's machine

learning operations pipeline as I

mentioned before AI is a different type

of workload and this type of new type of

software this new type of software has a

whole new software industry and this

software industry 100 of them we have

now connected with Nvidia Enterprise

I told you several things I told you

that we are going through two

simultaneous Computing industry

transition accelerated Computing and

generative AI

two

this form of computing is not like the

traditional general purpose Computing it

is full stack

it is Data Center scale because the data

center is the computer and it is domain

specific for every domain that you want

to go into every industry you go into

you need to have the software stack and

if you have the software stack then the

utility the utilization of your machine

the utilization of your computer will be

high

so number two

it is full stack data scanner scale and

domain specific we are in full

production of the engine of generative

Ai and that is hgx h100 meanwhile

this engine that's going to be used for

AI factories will be scaled out using

Grace Hopper the engine that we created

for the era of generative AI we also

took Grace Hopper connected to 256 node

nvlink and created the largest GPU in

the world dgx

gh200

we're trying to extend generative Ai and

accelerated Computing in several

different directions at the same time

number one we would like to of course

extend it in the cloud

so that every cloud data center can be

an AI data center not just AI factories

and hyperscale but every hyperscale data

center can now be a generative AI Data

Center and the way we do that is the

Spectrum X it takes four components to

make Spectrum X possible the switch

the Bluefield 3 Nick the interconnects

themselves the cables are so important

in high speed high-speed Communications

and the software stack that goes on top

of it we would like to extend generative

AI to the world's Enterprise and there

are so many different configurations of

servers and the way we're doing that

with partnership with our Taiwanese

ecosystem the mgx modular accelerated

Computing systems we put Nvidia into

Cloud so that every Enterprise in the

world can engage us to create generative

AI models and deploy it in a Enterprise

grade Enterprise secure way in every

single Cloud I want to thank all of you

for your partnership over the years

thank you

[Applause]