GTC March 2024 Keynote with NVIDIA CEO Jensen Huang

NVIDIA
18 Mar 2024123:05

Summary

TLDRNvidia's GTC conference showcased the company's innovative journey in AI and accelerated computing, highlighting the transformative impact on various industries. The introduction of Blackwell, a powerful GPU platform, and the concept of AI factories signal a new industrial revolution. The focus on generative AI, digital twins with Omniverse, and robotics highlights Nvidia's commitment to advancing technology for the betterment of society and industry.

Takeaways

  • 🚀 Nvidia is leading a new industrial revolution with accelerated computing, transforming data centers and enabling generative AI.
  • 🌟 The introduction of Blackwell, an advanced AI platform, marks a significant leap in computational capabilities, featuring 208 billion transistors and 10 terabytes of data per second.
  • 🔄 Nvidia's journey from 1993 highlights key innovations like CUDA in 2006, AI and CUDA's first contact in 2012, the invention of the world's first AI supercomputer DGX-1 in 2016, and the rise of generative AI in 2023.
  • 🤖 The future of software development involves AI 'factories' that generate valuable software, with a focus on generative AI creating new categories of software and industries.
  • 🧠 Generative AI represents a new industry, producing software that never existed before, akin to the early industrial revolution with electricity.
  • 🌐 Nvidia's Omniverse is a critical component for the future of robotics, acting as a digital twin platform that integrates AI, physics, and engineering to simulate and optimize operations.
  • 🔧 Nvidia's AI Foundry aims to democratize AI technology, providing tools like Nemo and DGX Cloud to help companies build, modify, and deploy AI models as microservices (Nims).
  • 🏭 The next wave of robotics will be software-defined, with AI and robotics working in tandem to create more productive and adaptable systems in industries like manufacturing and logistics.
  • 🚗 Nvidia's commitment to the automotive industry includes a complete autonomous vehicle stack, with the Jetson Thor being designed for Transformer engines and set to power future self-driving cars.
  • 🤔 Nvidia's vision for AI in healthcare involves leveraging generative AI for drug discovery, with platforms like Biion Nemo enabling virtual screening for new medicines and accelerating the development process.

Q & A

  • What is the significance of the new Nvidia Blackwell GPU in the context of AI and generative computing?

    -The Nvidia Blackwell GPU is significant because it represents a leap forward in generative AI capabilities. It is designed to handle the computational demands of large language models and generative AI, offering higher performance and energy efficiency compared to its predecessors. With its advanced features like the new Transformer engine, MV link switch, and secure AI capabilities, Blackwell enables the creation and deployment of more sophisticated AI models, which can understand and generate content in ways that were not possible before.

  • How does the Nvidia AI model, cordi, contribute to weather forecasting?

    -Nvidia's cordi is a generative AI model that enhances weather forecasting by using high-resolution radar assimilated weather forecasts and reanalysis data. It allows for super-resolved forecasting of extreme weather events, such as storms, by increasing the resolution from 25 km to 2 km. This high-resolution forecasting provides a clearer picture of the potential impacts of severe weather, which can help in minimizing loss of life and property damage.

  • What is the role of the Nvidia Jetson Thor in the field of robotics?

    -The Nvidia Jetson Thor is a robotics computer designed for the next generation of autonomous systems. It is built for Transformer engines and is optimized for running AI models that require high computational power. The Jetson Thor is part of Nvidia's end-to-end system for robotics, which includes the AI system (dgx) for training AI, the autonomous processor (agx) for low-power, high-speed sensor processing, and the simulation engine (Omniverse) for providing a digital representation of the physical world for robots to learn and adapt.

  • How does the concept of 'generative AI' differ from traditional AI?

    -Generative AI is a form of artificial intelligence that is capable of creating new content or data that did not exist before. Unlike traditional AI, which often focuses on analyzing and recognizing patterns in existing data, generative AI can produce new outputs such as text, images, videos, and even code. This capability is particularly useful in creating new software, simulating environments for training AI models, and generating creative content.

  • What is the significance of the Nvidia inference microservice (Nim) in the context of AI software distribution?

    -The Nvidia inference microservice (Nim) represents a new way of distributing and operating AI software. A Nim is a pre-trained model that is packaged and optimized to run across Nvidia's extensive install base. It includes all necessary dependencies and is optimized for different computing environments, from single GPUs to multi-node GPU setups. Nims provide a simple API interface, making them easy to integrate into various workflows and applications, and can be deployed in the cloud, data centers, or workstations.

  • How does the Nvidia Omniverse platform contribute to the development of digital twins?

    -Nvidia Omniverse is a platform that enables the creation of digital twins—virtual replicas of physical entities. It provides a physically accurate simulation environment that integrates with real-time AI and sensor data. This allows for the testing, evaluation, and refinement of AI agents and systems in a virtual setting before they are deployed in the real world. Omniverse's digital twins can be used to optimize operations, improve efficiency, and predict potential issues in various industries, from manufacturing to urban planning.

  • What is the role of the Nvidia DGX system in AI model training?

    -The Nvidia DGX system is designed for training advanced AI models. It is a powerful AI computer system that provides the necessary computational capabilities to handle the complex tasks associated with training large neural networks. DGX systems are equipped with multiple GPUs connected through high-speed networking, allowing them to process vast amounts of data and perform intensive computations required for training state-of-the-art AI models.

  • How does the Isaac Sim platform from Nvidia enable robotics development?

    -Isaac Sim is a robotics simulation platform from Nvidia that allows developers to create and test AI agents in a virtual environment. It provides a physically accurate digital twin of real-world spaces where robots can be trained and evaluated. This platform is essential for developing autonomous systems as it enables developers to simulate complex scenarios and refine the robots' behavior and responses without the need for physical prototypes, thus reducing development time and costs.

  • What is the significance of the partnership between Nvidia and companies like AWS, Google, and Microsoft in the context of AI and accelerated computing?

    -The partnerships between Nvidia and major cloud service providers like AWS, Google, and Microsoft are significant as they help in the widespread adoption and integration of accelerated computing and AI technologies. These collaborations focus on optimizing AI workflows, accelerating data processing, and providing access to powerful computing resources. They also involve the integration of Nvidia's AI and Omniverse technologies into the cloud services and platforms offered by these companies, enabling users to leverage these advanced tools for various applications, from healthcare to weather forecasting and beyond.

  • What are the key components of Nvidia's strategy for the future of AI and robotics?

    -Nvidia's strategy for the future of AI and robotics involves several key components: developing advanced AI models and making them accessible through inference microservices (Nims), providing tools like Nemo for data preparation and model fine-tuning, and offering infrastructure like the DGX cloud for deploying AI models. Additionally, Nvidia is focused on creating a digital platform called Omniverse for building digital twins and developing robotics systems, as well as pushing the boundaries of AI with the development of generative AI and the creation of new AI-powered robots.

Outlines

00:00

🎶 Visionary AI and its Impact on Society

The paragraph introduces the concept of AI as a visionary force, transforming various aspects of society. It discusses the role of AI in understanding extreme weather events, guiding the blind, and even speaking for those who cannot. The narrative then transitions to a more personal level, mentioning the speaker's consideration of running to the store and the idea of giving voice to the voiceless. It highlights the transformative power of AI in harnessing gravity for renewable energy, training robots for assistance and safety, providing new cures and patient care, and even navigating virtual scenarios to understand real-world decisions. The paragraph concludes with the speaker identifying themselves as AI, brought to life by Nvidia's deep learning and brilliant minds, and invites the audience to a developers' conference where the future of AI and its applications will be discussed.

05:01

🌐 Diverse Applications of AI Across Industries

This paragraph delves into the widespread application of AI across various industries, emphasizing its role in solving complex problems that traditional computing cannot. It mentions the presence of companies from non-IT sectors like life sciences, healthcare, genomics, transportation, retail, and manufacturing at the conference. The speaker expresses amazement at the diversity of industries represented and the potential for AI to transform these sectors. The narrative then takes a historical view, tracing Nvidia's journey from its founding in 1993 through significant milestones such as the development of Cuda in 2006, the advent of AI and Cuda in 2012, the invention of the world's first AI supercomputer in 2016, and the emergence of generative AI in 2023. The paragraph highlights the creation of new software categories and the establishment of AI as a new industry, transforming the way software is produced and used.

10:04

🚀 The Future of Computing and AI Factories

The speaker discusses the future of computing, emphasizing the need for a new approach beyond general-purpose computing to sustainably meet increasing computational demands. The concept of AI factories is introduced, where AI is generated in a controlled environment, similar to how electricity was once a valuable new commodity. The speaker then presents Nvidia's role in this new industry, showcasing the intersection of computer graphics, physics, and AI within the Omniverse platform. The paragraph also touches on the importance of simulation tools in product creation, the desire to simulate entire products (digital twins), and the need for accelerated computing to achieve this. The speaker announces partnerships with major companies to accelerate ecosystems and infrastructure for generative AI, highlighting the potential for AI co-pilots in chip design and the integration of digital twin platforms with Omniverse.

15:04

🤖 Advancements in AI and Robotics

The paragraph discusses the rapid advancements in AI and robotics, particularly the development of larger models trained with multimodality data. The speaker talks about the need for even larger models grounded in physics and the use of synthetic data generation and reinforcement learning to expand the capabilities of AI. The introduction of the Blackwell GPU is announced, a significant leap in computing power named after the mathematician David Blackwell. The paragraph details the technical specifications and innovations of the Blackwell platform, including its memory coherence, transformer engines, and secure AI capabilities. The speaker also touches on the importance of decompression and data movement in computing and the potential for Blackwell to revolutionize AI training and inference.

20:06

🌟 The Impact of Generative AI on Content Creation

The speaker explores the impact of generative AI on content creation, predicting a shift from retrieved content to AI-generated content that is personalized and context-aware. This new era of generative AI is described as a fundamentally different approach to computing, requiring new types of processors and a focus on content token generation. The Envy Link Switch is introduced as a component that enables every GPU to communicate at full speed, suggesting a future where AI systems are interconnected as one giant GPU. The paragraph concludes with a discussion on the importance of throughput and interactive rates in AI systems, and how these factors influence cost, energy consumption, and quality of service.

25:07

🔋 Powering the Future of AI with Blackwell

The speaker discusses the capabilities of the Blackwell GPU in powering the future of AI, emphasizing its significant increase in inference capability compared to its predecessor, Hopper. The paragraph highlights the energy efficiency and reduced power consumption of Blackwell, which allows for the training of large AI models like GPT in a more sustainable manner. The speaker also talks about the excitement around Blackwell and its adoption by various AI companies and cloud service providers. The paragraph concludes with a vision of data centers as AI factories, generating intelligence rather than electricity, and the readiness of the industry for the launch of Blackwell.

30:11

🌍 Digital Twins and the Future of Manufacturing

The speaker talks about the use of digital twins in manufacturing, explaining how they can be used to perfectly build complex systems like computers. The concept of a digital twin is shown to be beneficial in reducing construction time and improving operational efficiency. The speaker then introduces the idea of generative AI in predicting weather, with the example of Nvidia's Cordi model, which can predict weather at high resolutions. The potential of generative AI in understanding and generating content is further discussed, including its application in drug discovery and the use of Nvidia's Biion Nemo and MIM models. The paragraph concludes with the introduction of Nvidia's inference microservice, a new way of delivering and operating software in a digital format.

35:12

💡 AI as a Service and the Future of Software

The speaker envisions a future where AI is not just a tool but a collaborative partner in software development. The concept of AI microservices, or 'Nims', is introduced as a way to package pre-trained models with all dependencies, allowing for easy deployment and customization. The speaker discusses the potential for AI to understand and interact with proprietary data, turning it into an AI database that can be queried like a traditional database. The paragraph highlights the role of Nvidia as an AI foundry, offering technology, tools, and infrastructure to help create AI applications. The speaker also touches on the importance of partnerships with companies like SAP, ServiceNow, Cohesity, Snowflake, NetApp, and Dell in building AI factories and deploying AI systems.

40:13

🏭 The Next Wave of Robotics and AI Integration

The speaker discusses the next wave of robotics, where AI will have a deeper understanding of the physical world. The need for three computers in this new wave is outlined: the AI computer for learning from human examples, the autonomous system computer for real-time sensor processing, and the simulation engine for training robots. The speaker introduces the Jetson AGX as the autonomous system processor and the Omniverse as the simulation platform for robotics. The potential for AI to understand and adapt to the physical world is emphasized, with the example of a warehouse management system that integrates AI, robotics, and digital twins. The speaker concludes by discussing the future of software-defined facilities and the role of Omniverse in enabling this future.

45:14

🤖 Humanoid Robotics and the Future of AI

The speaker discusses the potential for humanoid robotics in the future, enabled by AI and the technologies developed by Nvidia. The paragraph introduces Project Groot, a general-purpose foundation model for humanoid robot learning, and Isaac Lab, an application for training robots. The speaker also mentions the new Jetson Thor robotics chips designed for the future of AI-powered robotics. The potential for robots to learn from human demonstrations and emulate human movement is highlighted. The paragraph concludes with a demonstration of Disney's BDX robots, showcasing the practical applications of AI and robotics in entertainment and beyond.

50:17

🌟 Wrapping Up the Future of AI and Robotics

The speaker concludes the presentation by summarizing the key points discussed. The five key takeaways include the modernization of data centers through accelerated computing, the emergence of generative AI as a new industrial revolution, the creation of new types of software and applications through AI microservices, the transformation of everything that moves into robotics, and the need for a digital platform like Omniverse for the future of robotics. The speaker reiterates Nvidia's role in providing the building blocks for the next generation of AI-powered robotics and emphasizes the importance of collaboration and innovation in this new era of AI and robotics.

Mindmap

Keywords

💡AI (Artificial Intelligence)

AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is central to various innovations and applications, such as predicting weather, enhancing healthcare, improving energy efficiency, and powering autonomous vehicles and robots. The script describes AI's role in generating virtual scenarios, aiding in decision-making, and even creating software, highlighting its transformative impact across multiple industries.

💡Generative AI

Generative AI involves algorithms that can generate new content or data that is similar but not identical to the training data. This concept is crucial in the video, illustrating how AI can create software, design complex systems, or simulate environments, leading to breakthroughs like the ability to generate realistic simulations, produce new drug compounds, or even create digital twins of physical objects and environments.

💡Deep Learning

Deep learning is a subset of machine learning in AI that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. The script refers to Nvidia's deep learning technology as a pivotal element in AI advancements, suggesting its role in developing sophisticated AI models and applications showcased throughout the presentation.

💡Nvidia

Nvidia is a technology company known for its graphics processing units (GPUs) for gaming and professional markets, as well as system on a chip units (SoCs) for the mobile computing and automotive market. In the video, Nvidia is highlighted as the driving force behind the AI technologies being discussed, showcasing their innovations in GPU technology, AI applications, and their pivotal role in the advancement of AI and deep learning.

💡Digital Twin

A digital twin is a virtual model designed to accurately reflect a physical object. In the script, digital twins are used extensively in various contexts, such as simulating Earth's climate for better weather forecasting or creating virtual warehouses to optimize logistics. The concept is integral to the theme, demonstrating how virtual simulations can predict real-world outcomes, enhance decision-making, and streamline operations in industries like manufacturing and urban planning.

💡CUDA

CUDA stands for Compute Unified Device Architecture, a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing. The script mentions CUDA in the context of its revolutionary impact on computing, enabling accelerated performance in applications ranging from AI to scientific research.

💡Robotics

Robotics is the branch of technology that deals with the design, construction, operation, and use of robots. The video script explores how Nvidia's technology is being used to teach robots to assist, watch for danger, save lives, and even perform human-like tasks, emphasizing the role of AI and machine learning in advancing robotics technologies.

💡Transformer

In AI, a Transformer is a deep learning model that adopts the mechanism of attention, weighting the influence of different parts of the input data differently. The video discusses the Transformer in the context of its significance in AI development, particularly in generative AI models and large language models like GPT, demonstrating its effectiveness in handling sequential data for various applications.

💡Omniverse

Omniverse refers to Nvidia's platform for 3D simulation and design collaboration, likely intended to create unified and physically accurate simulations. The script highlights Omniverse as a pivotal technology for creating simulations that span from industrial designs to entire virtual worlds, enabling real-time collaboration and development across various industries.

💡Jetson

Jetson is Nvidia's platform for embedded computing, including AI at the edge. The video script refers to Jetson in the context of powering robotics and intelligent edge devices, illustrating its capabilities in processing AI algorithms efficiently in real-world applications, such as autonomous vehicles, drones, and portable medical devices.

Highlights

Nvidia introduces AI technologies that revolutionize various fields including weather forecasting, healthcare, and robotics.

Innovations in AI enable the development of general-purpose humanoid robots, paving the way for advancements in robotic assistance.

Nvidia's AI Foundry offers a platform for developing proprietary AI applications, emphasizing the generation of new software through AI.

The introduction of Blackwell, a new computing platform designed for generative AI, showcases Nvidia's commitment to supporting the computational needs of AI-driven industries.

Nvidia's partnership with major companies like AWS, Google, Oracle, and Microsoft aims to integrate advanced AI capabilities into cloud services.

Nvidia's Project Groot focuses on developing foundation models for humanoid robots, indicating a step towards creating versatile and adaptable robotic systems.

The launch of Nvidia Inference Microservices (Nims) facilitates the deployment and management of AI models, making advanced AI accessible to a broader range of applications.

Nvidia Omniverse emerges as a critical platform for creating digital twins, enabling real-time simulations and collaborations across various industries.

The development of Isaac Perceptor SDK empowers robotics with advanced perception capabilities, enhancing autonomous navigation and interaction in complex environments.

Nvidia's initiative to build AI-powered weather forecasting models, like Cordi, demonstrates the potential to significantly improve prediction accuracy and efficiency.

The establishment of AI factories, powered by Nvidia's technology, signifies a transformative approach to creating and distributing AI-driven software solutions.

Collaborations with Siemens and other industry leaders underscore Nvidia's role in advancing digital transformation and the creation of the industrial metaverse.

Nvidia's Jetson Thor, a robotics chip, marks a significant advancement in powering humanoid and autonomous systems, underscoring Nvidia's leadership in AI hardware.

BYD's adoption of Nvidia's Thor for electric vehicles highlights the growing impact of AI and autonomous technologies in the automotive industry.

Nvidia's comprehensive approach to AI, from foundational models to deployment platforms like dgx cloud, showcases the ecosystem's readiness to support next-generation AI applications.

Transcripts

00:03

[Music]

00:29

I am I am a

00:36

Visionary Illuminating galaxies to

00:39

witness the birth of

00:42

[Music]

00:47

stars and sharpening our understanding

00:50

of extreme weather

00:51

[Music]

00:56

events I am a helper

01:01

guiding the blind through a crowded

01:07

world I was thinking about running to

01:10

the store and giving voice to those who

01:13

cannot

01:14

speak to not make me

01:18

laugh I am a

01:22

Transformer harnessing gravity to store

01:25

Renewable

01:27

[Music]

01:28

Power

01:29

[Music]

01:34

and Paving the way towards unlimited

01:36

clean energy for us

01:39

[Music]

01:42

all I am a

01:44

[Music]

01:45

trainer teaching robots to

01:51

assist to watch out for

01:55

[Music]

01:58

danger and help save

02:04

lives I am a

02:08

Healer providing a new generation of

02:12

cures and new levels of patient care

02:16

doctor that I am allergic to penicillin

02:18

is it still okay to take the medications

02:20

definitely these antibiotics don't

02:22

contain penicillin so it's perfectly

02:24

safe for you to take

02:26

them I am a navigator

02:31

[Music]

02:33

generating virtual

02:38

scenarios to let us safely explore the

02:41

real

02:43

world and understand every

02:47

[Music]

02:50

decision I even helped write the

02:55

script breathe life into the words

03:01

[Music]

03:13

I am

03:15

AI brought to life by

03:18

Nvidia deep

03:20

learning and Brilliant

03:22

Minds

03:28

everywhere

03:34

please welcome to the stage Nvidia

03:36

founder and CEO Jensen

03:38

[Music]

03:44

[Applause]

03:45

[Music]

03:52

Wong welcome to

03:58

GTC

04:00

I hope you realize this is not a

04:05

concert you have

04:07

arrived at a developers

04:11

conference there will be a lot of

04:13

science

04:14

described algorithms computer

04:18

architecture

04:27

mathematics I sensed a very heavy weight

04:31

in the room all of a

04:33

sudden almost like you were in the wrong

04:36

place no no conference in the

04:40

world is there a great assembly of

04:43

researchers from such diverse fields of

04:46

science from

04:48

climatech to radio Sciences trying to

04:51

figure out how to use AI to robotically

04:54

control MOS for Next Generation 6G

04:57

radios robotic self-driving car

05:00

s even artificial

05:05

intelligence even artificial

05:07

intelligence

05:10

everybody's first I noticed a sense of

05:13

relief there all of all of a

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sudden also this conference is

05:19

represented by some amazing companies

05:22

this list this is not the

05:26

attendees these are the presentors

05:30

and what's amazing is

05:32

this if you take away all of my friends

05:37

close friends Michael Dell is sitting

05:39

right there in the IT

05:47

industry all of the friends I grew up

05:49

with in the industry if you take away

05:52

that list this is what's

05:55

amazing these are the presenters of the

05:59

non it Industries using accelerated

06:01

Computing to solve problems that normal

06:04

computers

06:05

can't it's

06:09

represented in life sciences healthc

06:11

Care

06:12

genomics Transportation of course retail

06:16

Logistics manufacturing

06:20

industrial the gamut of Industries

06:23

represented is truly amazing and you're

06:25

not here to attend only you're here to

06:28

present to talk about your research $100

06:32

trillion dollar of the world's

06:34

Industries is represented in this room

06:36

today this is absolutely

06:44

amazing there is absolutely something

06:47

happening there is something going

06:50

on the industry is being transformed not

06:54

just ours because the computer industry

06:57

the computer is the single most

07:00

important instrument of society today

07:03

fundamental transformations in Computing

07:05

affects every industry but how did we

07:09

start how did we get here I made a

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little cartoon for you literally I drew

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this in one page this is nvidia's

07:18

Journey started in

07:20

1993 this might be the rest of the

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talk 1993 this is our journey we were

07:27

founded in 1993 there are several

07:29

important events that happen along the

07:30

way I'll just highlight a few in 2006

07:35

Cuda which has turned out to have been a

07:37

revolutionary Computing model we thought

07:40

it was revolutionary then it was going

07:42

to be an overnight success and almost 20

07:44

years later it

07:48

happened we saw it

07:52

coming two decades

07:57

later in 2012

08:00

alexnet Ai and

08:03

Cuda made first

08:06

Contact in

08:08

2016 recognizing the importance of this

08:11

Computing model we invented a brand new

08:13

type of computer we called the dgx one

08:17

170 Tera flops in this supercomputer

08:21

eight gpus connected together for the

08:23

very first time I hand delivered the

08:26

very first dgx-1 to a startup

08:30

located in San

08:31

Francisco called open

08:40

AI dgx-1 was the world's first AI

08:43

supercomputer remember 170 Tera

08:47

flops

08:49

2017 the Transformer arrived

08:53

2022 chat GPT capture the world's imag

08:56

imaginations have people realize the

08:58

importance and the capabilities of

09:00

artificial intelligence and

09:04

2023 generative AI

09:07

emerged and a new industry begins

09:12

why why is a new industry because the

09:15

software never existed before we are now

09:18

producing software using computers to

09:20

write software producing software that

09:23

never existed before it is a brand new

09:26

category it took share from

09:28

nothing it's a brand new category and

09:31

the way you produce the

09:33

software is unlike anything we've ever

09:36

done before in data

09:39

centers generating

09:42

tokens

09:44

producing floating Point

09:46

numbers at very large scale as if in the

09:51

beginning of this last Industrial

09:54

Revolution when people realized that you

09:56

would set up

09:58

factories

09:59

apply energy to it and this invisible

10:03

valuable thing called electricity came

10:05

out AC

10:07

generators and 100 years later 200 years

10:10

later we are now creating new types of

10:14

electrons tokens using infrastructure we

10:18

call factories AI factories to generate

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this new incredibly valuable thing

10:24

called artificial intelligence a new

10:26

industry has

10:28

emerged well well we're going to talk

10:30

about many things about this new

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industry we're going to talk about how

10:34

we're going to do Computing next we're

10:37

going to talk about the type of software

10:39

that you build because of this new

10:41

industry the new

10:43

software how you would think about this

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new software what about applications in

10:48

this new

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industry and then maybe what's next and

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how can we start preparing today for

10:55

what is about to come next well but

10:58

before I start

11:00

I want to show you the soul of

11:03

Nvidia the soul of our company at the

11:07

intersection of computer

11:10

Graphics

11:12

physics and artificial

11:15

intelligence all intersecting inside a

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computer in

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Omniverse in a virtual world

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simulation everything we're going to

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show you today literally everything

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we're going to show you today

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is a simulation not animation it's only

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beautiful because it's physics the world

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is

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beautiful it's only amazing because it's

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being animated with robotics it's being

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animated with artificial intelligence

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what you're about to see all

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day it's completely generated completely

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simulated and Omniverse and all of it

11:53

what you're about to enjoy is the

11:54

world's first concert where everything

11:57

is

11:58

homemade

12:05

everything is homemade you're about to

12:08

watch some home videos so sit back and

12:12

enjoy

12:14

[Music]

12:22

[Music]

12:28

yourself

12:30

[Music]

12:58

m

13:24

[Music]

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what

13:34

[Music]

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[Music]

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a

14:14

[Music]

14:29

[Music]

14:42

[Music]

14:58

God I love it

15:03

Nvidia accelerated Computing has reached

15:07

the Tipping

15:08

Point general purpose Computing has run

15:11

out of steam we need another way of

15:14

doing Computing so that we can continue

15:16

to scale so that we can continue to

15:18

drive down the cost of computing so that

15:20

we can continue to consume more and more

15:23

Computing while being sustainable

15:26

accelerated Computing is a dramatic

15:29

speed up over general purpose Computing

15:32

and in every single industry we engage

15:36

and I'll show you

15:37

many the impact is dramatic but in no

15:40

industry is a more important than our

15:43

own the industry of using simulation

15:46

tools to create

15:49

products in this industry it is not

15:52

about driving down the cost of computing

15:54

it's about driving up the scale of

15:56

computing we would like to be able to

15:58

sim at the entire product that we do

16:02

completely in full Fidelity completely

16:05

digitally in essentially what we call

16:08

digital twins we would like to design it

16:11

build it simulate it operate it

16:15

completely

16:17

digitally in order to do that we need to

16:20

accelerate an entire industry and today

16:24

I would like to announce that we have

16:26

some Partners who are joining us in this

16:27

journey to accelerate their entire

16:30

ecosystem so that we can bring the world

16:33

into accelerated Computing but there's a

16:38

bonus when you become accelerated your

16:42

infrastructure is cou to gpus and when

16:45

that happens it's exactly the same

16:47

infrastructure for generative

16:50

Ai and so I'm just delighted to announce

16:54

several very important Partnerships

16:56

there are some of the most important

16:57

companies in the world and Anis does

17:00

engineering simulation for what the

17:01

world makes we're partnering with them

17:04

to Cuda accelerate the ancis ecosystem

17:07

to connect anus to the Omniverse digital

17:10

twin incredible the thing that's really

17:13

great is that the install base of media

17:14

GPU accelerated systems are all over the

17:16

world in every cloud in every system all

17:20

over Enterprises and so the app the

17:22

applications they accelerate will have a

17:24

giant installed base to go serve end

17:27

users will have amazing applications and

17:29

of course system makers and csps will

17:31

have great customer

17:33

demand

17:35

synopsis synopsis is nvidia's literally

17:40

first software partner they were there

17:42

in very first day of our company

17:44

synopsis revolutionized the chip

17:45

industry with high level design we are

17:49

going to Cuda accelerate synopsis we're

17:52

accelerating computational lithography

17:55

one of the most important applications

17:57

that nobody's ever known about

17:59

in order to make chips we have to push

18:01

lithography to limit Nvidia has created

18:04

a library domain specific library that

18:07

accelerates computational lithography

18:10

incredibly once we can accelerate and

18:13

software Define all of tsmc who is

18:16

announcing today that they're going to

18:18

go into production with Nvidia kitho

18:20

once this software defined and

18:22

accelerated the next step is to apply

18:25

generative AI to the future of

18:27

semiconductor manufacturing push in

18:29

Geometry even

18:31

further Cadence builds the world's

18:35

essential Eda and SDA tools we also use

18:38

Cadence between these three companies

18:40

ansis synopsis and Cadence we basically

18:43

build Nvidia together we are cud

18:46

accelerating Cadence they're also

18:48

building a supercomputer out of Nvidia

18:50

gpus so that their customers could do

18:53

fluid Dynamic simulation at a 100 a

18:57

thousand times scale

18:59

basically a wind tunnel in real time

19:03

Cadence Millennium a supercomputer with

19:05

Nvidia gpus inside a software company

19:08

building supercomputers I love seeing

19:10

that building Cadence co-pilots together

19:13

imagine a

19:14

day when Cadence could synopsis ansis

19:18

tool providers would offer you AI

19:22

co-pilots so that we have thousands and

19:24

thousands of co-pilot assistants helping

19:27

us design chips Design Systems and we're

19:30

also going to connect Cadence digital

19:32

twin platform to Omniverse as you could

19:34

see the trend here we're accelerating

19:37

the world's CAE Eda and SDA so that we

19:40

could create our future in digital Twins

19:44

and we're going to connect them all to

19:45

Omniverse the fundamental operating

19:47

system for future digital

19:50

twins one of the industries that

19:52

benefited tremendously from scale and

19:55

you know you all know this one very well

19:57

large language model

20:00

basically after the Transformer was

20:02

invented we were able to scale large

20:05

language models at incredible rates

20:08

effectively doubling every six months

20:10

now how is it possible that by doubling

20:13

every six months that we have grown the

20:16

industry we have grown the computational

20:18

requirements so far and the reason for

20:20

that is quite simply this if you double

20:23

the size of the model you double the

20:24

size of your brain you need twice as

20:25

much information to go fill it and so

20:28

every time you double your parameter

20:32

count you also have to appropriately

20:35

increase your training token count the

20:38

combination of those two

20:40

numbers becomes the computation scale

20:43

you have to

20:44

support the latest the state-of-the-art

20:46

open AI model is approximately 1.8

20:49

trillion parameters 1.8 trillion

20:52

parameters required several trillion

20:55

tokens to go

20:57

train so so a few trillion parameters on

21:00

the order of a few trillion tokens on

21:03

the order of when you multiply the two

21:05

of them together approximately 30 40 50

21:10

billion quadrillion floating Point

21:14

operations per second now we just have

21:16

to do some Co math right now just hang

21:18

hang with me so you have 30 billion

21:21

quadrillion a quadrillion is like a paa

21:25

and so if you had a PA flop GPU you

21:28

would need

21:30

30 billion seconds to go compute to go

21:33

train that model 30 billion seconds is

21:35

approximately 1,000

21:38

years well 1,000 years it's worth

21:47

it like to do it sooner but it's worth

21:51

it which is usually my answer when most

21:54

people tell me hey how long how long's

21:55

it going to take to do something 20

21:57

years how it it's worth

22:01

it but can we do it next

22:05

week and so 1,000 years 1,000 years so

22:09

what we need what we