- The speaker describes themselves as a visionary, helper, transformer, trainer, healer, and navigator, showcasing the impact of technology on various industries.
- The speaker highlights the importance of AI and accelerated computing in solving complex problems across different sectors.
- The speaker discusses the history and evolution of their company, particularly in the development of AI and the emergence of a new industry.
- The speaker emphasizes the transformative power of artificial intelligence and the creation of a new category of software.

- The speaker discusses the future of computing and the impact of accelerated computing in various industries
- They emphasize the importance of accelerating the simulation tools industry and announce partnerships with companies like Ansys, Synopsys, and Cadence
- The focus is on accelerating the entire ecosystem to create digital twins and connect them to Omniverse
- The speaker also discusses the growth of large language models and the increasing computational requirements for training them

- The open AI model has 1.8 trillion parameters and requires several trillion tokens to train
- This requires approximately 30-50 billion quadrillion floating point operations per second
- To support this, larger GPUs and supercomputers have been developed, such as CELINE and Eos
- The goal is to train larger models with multimodality data, including text, images, graphs, and videos
- This will involve using synthetic data generation and reinforcement learning
- A new platform named Blackwell, with 208 billion transistors, has been introduced as the most advanced GPU in production

- The Blackwell chip has 208 billion transistors and 10 terabytes of data per second
- It can be used in two types of systems, Hopper and Grace Blackwell
- The chip has a new transformer engine, a reliability engine, and secure AI capabilities
- It has improved performance for training and inference tasks, and can handle large amounts of data efficiently

- The use of AI in computing is shifting from retrieval to generation
- The future of computing is generative AI, leading to a new industry
- The advancement in computing power has increased by 1,000 times in the last 8 years
- A new chip called NVLink switch has been developed to connect GPUs at full speed
- The DGX system is now 720 petaflops, almost an exaflop for training
- The system is liquid-cooled and has 5,000 NVLink cables, saving 20 kilowatts for computation
- The GPUs now have 600,000 parts and weigh 3,000 pounds
- Training a GPT model with Hopper would require 8,000 GPUs and consume 15 megawatts

- The speaker discusses the use of GPUs and power consumption for training groundbreaking AI models
- Blackwell is introduced as a system that requires fewer GPUs and less power for the same training time
- The importance of efficient token generation for large language models is highlighted
- The programmability of NVIDIA's GPUs allows for exploration of different configurations and performance optimization
- Blackwell's inference capability is compared to Hopper, showing significant improvement
- The excitement and potential of Blackwell for AI companies is emphasized

- The speaker discusses the impact of technology on various industries and the importance of AI and accelerated computing in solving complex problems
- They highlight the history and evolution of their company, emphasizing the transformative power of artificial intelligence and the future of computing
- The focus is on accelerating the entire ecosystem to create digital twins and connect them to Omniverse
- The speaker discusses the growth of large language models and the increasing computational requirements for training them
- A new platform named Blackwell, with 208 billion transistors, has been introduced as the most advanced GPU in production
- The use of AI in computing is shifting from retrieval to generation, leading to a new industry
- The advancement in computing power has increased by 1,000 times in the last 8 years
- The speaker discusses the use of GPUs and power consumption for training groundbreaking AI models, highlighting the importance of efficient token generation for large language models
- The programmability of NVIDIA's GPUs allows for exploration of different configurations and performance optimization
- The excitement and potential of Blackwell for AI companies is emphasized.

- Blackwell is being launched as the most successful product in the company's history
- AWS, Google, Oracle, and Microsoft are all gearing up for Blackwell
- Wistron is using digital twins to improve efficiency and reduce production time
- The speaker discusses the advancements in AI and its ability to understand and generate various forms of data
- The potential for generative AI to revolutionize various industries, including climate prediction and extreme weather forecasting

- NVIDIA has created Earth 2, a digital twin of the Earth for predicting weather
- They have developed CORDIF, a generative AI model that can predict weather at extremely high resolution
- This technology can accurately predict and track severe storms, with 1000 times the speed and 3000 times the energy efficiency of conventional weather models
- They are also working on AI models for medical imaging, gene sequencing, and computational chemistry
- NVIDIA is introducing BioNemo NIMS for drug discovery, enabling the generation and screening of candidate molecules in minutes
- They are also creating NVIDIA Inference Microservices (NIMS) to package and optimize pre-trained AI models for easy integration into workflows
- NIMS will be available on ai.nvidia.com and can be used in any cloud or data center.

- NVIDIA has developed NIMS (NVIDIA Inference Microservices) that can perform various tasks such as numerical analysis and data processing
- These NIMS can work together as a team and do amazing things, increasing productivity for chip designers
- NVIDIA also offers Nemo microservice to help curate and teach AI, and DGX Cloud for deploying AI models
- The company is working with industry giants like SAP, ServiceNow, Cohesity, Snowflake, and NetApp to build virtual assistants and AI agents
- NVIDIA is also partnering with Dell to provide AI factories for enterprise-scale AI deployment
- The next wave of AI includes physical robotics and advanced AI technologies

- The next wave of AI and robotics will involve three computers working together
- The AI computer will watch videos and human examples to understand the physical world
- The Jetson computer will be used for autonomous processing in robotics
- The OVX computer will run the simulation engine for the robot to learn in a virtual world
- The Omniverse digital twin will be used for testing and refining AI agents in a simulated environment
- Siemens is integrating NVIDIA AI and Omniverse technologies into their industrial platform, Teamcenter X

- Siemens and NVIDIA are working together to integrate Omniverse APIs with Teamcenter X for interactive visualization of large engineering datasets
- The result is an ultra-intuitive, photo-real, physics-based digital twin that eliminates waste and errors, delivering huge savings in cost and time
- Omniverse is being integrated into workflows at companies like Nissan and BYD for automotive and robotics applications
- NVIDIA is also developing advanced robotics technology, including the Isaac Perceptor SDK for adaptive robot navigation and the CUDA-accelerated motion planner for manipulator arms
- The next generation of robotics may include humanoid robots, leveraging imitation training data to operate more effectively in human environments

- NVIDIA is developing a humanoid robot named Groot, which can learn from human demonstrations and natural language instructions
- They have created Isaac Lab, Osmo, and Groot as building blocks for AI-powered robotics
- The new Jetson Thor robotics chips power Groot and are designed for the future
- NVIDIA is also working on a new industrial revolution, generative AI, new types of software distribution, and a digital twin platform called Omniverse for the robotics world

- The speaker is impressed with the processors, NVLink switches, networking systems, and system design of Blackwell
- They describe Blackwell as what a GPU looks like in their mind
- The audience applauds
- The speaker hints at one more treat for the audience

- Blackwell is being launched as the most successful product in the company's history
- AWS, Google, Oracle, and Microsoft are all gearing up for Blackwell
- Wistron is using digital twins to improve efficiency and reduce production time
- NVIDIA has developed advanced AI models for weather prediction, medical imaging, and drug discovery
- NVIDIA is introducing BioNemo NIMS for drug discovery and NVIDIA Inference Microservices (NIMS) for easy integration of AI models
- NVIDIA is working with industry giants to build virtual assistants and AI agents
- The next wave of AI includes physical robotics and advanced AI technologies
- NVIDIA is integrating AI and Omniverse technologies into industrial platforms
- NVIDIA is developing advanced robotics technology, including humanoid robots and new robotics chips
- The speaker is impressed with the processors, NVLink switches, networking systems, and system design of Blackwell
- The audience applauds
- The speaker hints at one more treat for the audience

Tune in at 1:00pm PT / 4:00pm ET when Nvidia CEO Jensen Huang kicks off its biannual GTC conference.

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Illuminating galaxies to witness the birth of stars.

♪ ♪ And sharpening our understanding of extreme weather 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.

I am a transformer, Harnessing gravity to store renewable power.

And paving the way towards unlimited clean energy for us all.

I am a trainer, teaching robots to assist, 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 medications?

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.

Please welcome to the stage, NVIDIA founder and CEO, Jensen Wang.

I hope you realize this is not a concert.

You have arrived at a developer's 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 conference in the world is there a greater assembly of researchers from such diverse fields of science, from climate tech to radio sciences, trying to figure out how to use AI to robotically control MIMOs for next-generation 6G radios, robotics, self-driving cars, even artificial intelligence.

First, I noticed a sense of relief there all of a sudden.

Also, this conference is represented by some amazing companies.

If you take away all of my friends, close friends.

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 life sciences, healthcare, 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 of the world's industries is represented in this room today.

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.

This is a video's journey started in 1993.

There are several important events that happened along the way.

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 an overnight success, and almost 20 years later, it happened.

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.

8 GPUs connected together for the very first time.

I hand-delivered the very first DGX-1 to a startup located in San Francisco called OpenAI.

DGX-1 was the world's first AI supercomputer.

2022, chat GPT captured the world's imaginations, helped people realize the importance and the capabilities of artificial intelligence.

In 2023, generative AI emerged And a new industry begins.

Because the software never existed before.

We are now producing software, using computers to write software, producing software that never existed before.

And the way you produce this 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.

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 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 gonna show you today, literally everything we're gonna show you today, is a simulation, not animation.

It's only beautiful because it's physics.

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, and omniverse.

And all of it, what you're about to enjoy, is the world's first concert where everything is homemade.

♪♪ ♪♪ ♪♪ ♪♪ ♪♪ ♪♪

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 speedup 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 it 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, in 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.

Your infrastructure is couped 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.

Ansys does engineering simulation for what the world makes.

We're partnering with them to CUDA accelerate the Ansys ecosystem, to connect Ansys to the Omniverse digital twin.

The thing that's really great is that the install base of NVIDIA GPU accelerated systems are all over the world, in every cloud, in every system, all over enterprises, and so the applications they accelerate will have a giant install base to go serve.

End users will have amazing applications.

And of course, system makers and CSPs will have great customer demand.

Synopsys is NVIDIA's literally first software partner.

They were there in the very first day of our company.

Synopsys revolutionized the chip industry with high-level design.

We are going to CUDA-accelerate Synopsys.

We're accelerating computational lithography, 1 of the most important applications that nobody's ever known about.

In order to make chips, we have to push lithography to a limit.

NVIDIA has created a library, a 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 QLiTho.

Once it's 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 EDA and SDA tools.

Between these 3 companies, Ansys, Synopsys, and Cadence, we basically build NVIDIA.

Together, we are couped to accelerating cadence.

They're also building a supercomputer out of NVIDIA GPUs so that their customers could do fluid dynamic simulation at a thousand times scale.

Cadence Millennium, a supercomputer with NVIDIA GPUs inside.

A software company building supercomputers.

Imagine a day when Cadence could synopsis, Ansys, 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 can 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.

1 of the industries that benefited tremendously from scale, and you all know this 1 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 6 months.

Now, how is it possible that by doubling every 6 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 2 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 2 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.

And so if you had a PETA flop GPU, you would need 30 billion seconds to go compute, to go train that model.

30 billion seconds is approximately 1,000 years.

I'd like to do it sooner, but it's worth it.

It's usually my answer when most people tell me, hey, how long is it going to take to do something?

So what we need, what we need are 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 NVLink so that we could create essentially virtually giant GPUs, and connecting them all together with amazing networks from a company called Mellanox, Infiniband, so that we could create these giant systems.

And so DGX-1 was our first version, but it wasn't the last.

We built supercomputers all the way, all along the way.

In 2021, we had CELINE, 4,500 GPUs or so.

And then in 2023, we built 1 of the largest AI supercomputers in the world.

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've got to build them first.

We build the chips, the systems, the networking, all of the software necessary to do this.

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 energy efficiency, the best computation time, keep your costs down.

And so those fundamental innovations is what got us here.

And here we are, as we see the miracle of chat GPT emerge in front of us, we also realize we have a long ways to go.

We're going to train it with multimodality data, not just text on the Internet, but we're going to train it on text and images and graphs and charts.

And so there's going to be a whole bunch of watching video so that these 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.

They'll use things like synthetic data generation, just as you and I do, 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 hope it's going to turn out as well as I had it 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.

If I get a low wind at about 10 minutes into this, you know what happened.

We're sitting here using synthetic data generation.

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 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.

A mathematician, game theorist, probability.

We thought it was a perfect, perfect name.

Blackwell, Ladies and gentlemen, enjoy this.

People think we make GPUs, and we do, but GPUs don't look the way they used to.

Here's the, if you will, the heart of the Blackwell system.

And this inside the company is not called Blackwell, it's just a number.

And this is Blackwell sitting next to, oh, this is the most advanced GPU in the world in production today.

And so you could see, I can see, there's a small line between 2 dies.

This is the first time 2 dyes have abutted like this together in such a way that the 2 dyes think it's 1 chip.

There's 10 terabytes of data between it, 10 terabytes per second, so that these 2 sides of the Blackwell chip have no clue which side they're on.

There's no memory locality issues, no cache issues.

And so when we were told that Blackwell's ambitions were beyond the limits of physics, the engineer said, so what?

And so this is the Blackwell chip, and it goes into 2 types of systems.

The first 1 is form-fit function compatible to hopper.

And so you slide on hopper and you push in black.

Well, that's the reason why 1 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.

And so this is a fully functioning board, and I'll just be careful here.

This right here is, I don't know, $10 billion?

It gets cheaper after that, so any customers in the audience, it's okay.

All right, but this is this one's quite expensive.

And the way it's going to go to production is like this 1 here.

It has 2 Blackwell chips and 4 Blackwell dies connected to a Grace CPU.

The Grace CPU has a super fast chip-to-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.

They feel like they're just 1 big happy family working on 1 application together.

Just the amount of, you know, you saw the numbers.

There's a lot of terabytes this and terabytes that.

This is a, this, let's see, what are some of the things on here?

There's MV link on top, PCI Express on the bottom.

It's my left or your, depending on which side.

I was just trying to sort that out, and I just kind of...

OK, so this is the Grace Blackwell system.

So it turns out, it turns out, All of the specs is fantastic, but we need a whole lot of new features.

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 1 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 recast numerical formats to a lower precision whenever it 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.

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