NVIDIA CEO sees bright future for AI-powered electric grid

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NVIDIA founder and CEO Jensen Huang said at the Edison Electric Institute (EEI) annual meeting on Tuesday that the next industrial revolution for the electric grid and the utilities that manage it is AI and fast. Speed ​​plays an important role in running through computing. Association of American and International Utilities

“The future of digital intelligence is very bright, and so is the future of the energy sector,” Huang said in a talk to an audience of more than 1,000 utility and energy industry executives.

Like other companies, utilities will also apply AI to increase employee productivity, but “the biggest impact and return is in applying AI to energy delivery to the grid,” said Huang, EEI's chair and president. And said in conversation with Pedro Pizarro. CEO of Edison International, parent company of Southern California Edison, one of the nation's largest electric utilities.

For example, Huang described how grids will use AI-powered smart meters to allow customers to sell their excess electricity to neighbors.

“You'll connect resources and users like Google, so your power grid will become a smart network with a digital layer like an app store for energy,” he said.

“My sense is, like previous industrial revolutions, [AI] will take productivity to levels we have never seen,” he added.

The video of this conversation will be available here soon.

AI light up electric grids

Today, electric grids are essentially unidirectional systems that connect a few large power plants to many consumers. They will increasingly become bidirectional, flexible and distributed networks with solar and wind farms connecting homes and buildings that sport solar panels, batteries and electric vehicle chargers.

This is a large task that requires an autonomous control system that processes and analyzes large amounts of data in real time—a task well suited to AI and high-speed computing.

AI is being applied to use cases in electric grids thanks to a wide ecosystem of companies using NVIDIA's technologies.

At a recent GTC session, utility vendor Hubble and startup Utilidata, a member of the NVIDIA Inception program, described a new generation of smart meters using the NVIDIA Jetson platform that utilities use AI models to create real-time grids. will deploy to process and analyze the data. the edge. Deloitte today announced its support for the effort.

In a separate GTC session, Siemens Energy detailed its work with AI and NVIDIA Omniverse to create digital twins of transformers in substations to improve predictive maintenance, increase grid resilience. And a video reports how Siemens Gamesa used Omniverse and accelerated computing to optimize turbine locations for a large wind farm.

“Deployment of AI and advanced computing technologies developed by NVIDIA enables faster and better grid modernization and, in turn, , we can provide to our customers.”

NVIDIA provides a 45,000x gain in energy efficiency.

The advancement comes as NVIDIA reduces the costs and energy required to deploy AI.

Huang said in his recent keynote at COMPUTEX that over the past eight years, NVIDIA has increased the energy efficiency of running AI inference on the latest large language models by 45,000x.

NVIDIA Blackwell Architecture GPUs will deliver 20x more energy efficiency than CPUs for AI and high-performance computing. If all CPU servers for these jobs moved to GPUs, consumers would save 37 terawatt-hours a year, equivalent to 25 million metric tons of carbon dioxide and the electricity use of 5 million homes.

That's why NVIDIA-powered systems took the top six spots and seven of the top 10 in the latest Green500 rankings, the world's most energy-efficient supercomputers. There is a list of computers.

In addition, a recent report called for governments to accelerate the adoption of AI as a key new tool for increasing energy efficiency in many industries. He cited examples of utilities adopting AI to make the electric grid more efficient.

Learn more about how utilities are deploying AI and accelerated computing to improve operations, save costs and save energy.

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