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We Created a Processor for the Generative AI Era,' NVIDIA CEO Says


Generative AI promises to revolutionize every industry it touches - all that's been needed is the technology to meet the challenge.

NVIDIA founder and CEO Jensen Huang on Monday introduced that technology - the company's new Blackwell computing platform - as he outlined the major advances that increased computing power can deliver for everything from software to services, robotics to medical technology and more.

Accelerated computing has reached the tipping point - general purpose computing has run out of steam, Huang told more than 11,000 GTC attendees gathered in-person - and many tens of thousands more online - for his keynote address at Silicon Valley's cavernous SAP Center arena.

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, in every single industry.

Huang spoke in front of massive images on a 40-foot tall, 8K screen the size of a tennis court to a crowd packed with CEOs and developers, AI enthusiasts and entrepreneurs, who walked together 20 minutes to the arena from the San Jose Convention Center on a dazzling spring day.

Delivering a massive upgrade to the world's AI infrastructure, Huang introduced the NVIDIA Blackwell platform to unleash real-time generative AI on trillion-parameter large language models.

Huang presented NVIDIA NIM - a reference to NVIDIA inference microservices - a new way of packaging and delivering software that connects developers with hundreds of millions of GPUs to deploy custom AI of all kinds.

And bringing AI into the physical world, Huang introduced Omniverse Cloud APIs to deliver advanced simulation capabilities.

Huang punctuated these major announcements with powerful demos, partnerships with some of the world's largest enterprises and more than a score of announcements detailing his vision.

GTC - which in 15 years has grown from the confines of a local hotel ballroom to the world's most important AI conference - is returning to a physical event for the first time in five years.

This year's has over 900 sessions - including a panel discussion on transformers moderated by Huang with the eight pioneers who first developed the technology, more than 300 exhibits and 20-plus technical workshops.

It's an event that's at the intersection of AI and just about everything. In a stunning opening act to the keynote, Refik Anadol, the world's leading AI artist, showed a massive real-time AI data sculpture with wave-like swirls in greens, blues, yellows and reds, crashing, twisting and unraveling across the screen.

As he kicked off his talk, Huang explained that the rise of multi-modal AI - able to process diverse data types handled by different models - gives AI greater adaptability and power. By increasing their parameters, these models can handle more complex analyses.

But this also means a significant rise in the need for computing power. And as these collaborative, multi-modal systems become more intricate - with as many as a trillion parameters - the demand for advanced computing infrastructure intensifies.

We need even larger models, Huang said. We're going to train it with multimodality data, not just text on the internet, we're going to train it on texts and images, graphs and charts, and just as we learned watching TV, there's going to be a whole bunch of watching video.

The Next Generation of Accelerated Computing In short, Huang said we need bigger GPUs. The Blackwell platform is built to meet this challenge. Huang pulled a Blackwell chip out of his pocket and held it up side-by-side with a Hopper chip, which it dwarfed.

Named for David Harold Blackwell - a University of California, Berkeley mathematician specializing in game theory and statistics, and the first Black scholar inducted into the National Academy of Sciences - the new architecture succeeds the NVIDIA Hopper architecture, launched two years ago.

Blackwell delivers 2.5x its predecessor's performance in FP8 for training, per chip, and 5x with FP4 for inference. It features a fifth-generation NVLink interconnect that's twice as fast as Hopper and scales up to 576 GPUs.

And the NVIDIA GB200 Grace Blackwell Superchip connects two Blackwell NVIDIA B200 Tensor Core GPUs to the NVIDIA Grace CPU over a 900GB/s ultra-low-power NVLink chip-to-chip interconnect.

Huang held up a board with the system. This computer is the first of its kind where this much computing fits into this small of a space, Huang said. Since this is memory coherent, they feel like it's one big happy family working on one application together.

For the highest AI performance, GB200-powered systems can be connected with the NVIDIA Quantum-X800 InfiniBand and Spectrum-X800 Ethernet platforms, also announced today, which deliver advanced networking at speeds up to 800Gb/s.

The amount of energy we save, the amount of networking bandwidth we save, the amount of wasted time we save, will be tremendous, Huang said. The future is generative which is why this is a brand new industry. The way we compute is fundamentally different. We created a processor for the generative AI era.

To scale up Blackwell, NVIDIA built a new chip called NVLink Switch. Each can connect four NVLink interconnects at 1.8 terabytes per second and eliminate traffic by doing in-network reduction.

NVIDIA Switch and GB200 are key components of what Huang described as one giant GPU, the NVIDIA GB200 NVL72, a multi-node, liquid-cooled, rack-scale system that harnesses Blackwell to offer supercharged compute for trillion-parameter models, with 720 petaflops of AI training performance and 1.4 exaflops of AI inference perform
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