NVIDIA: Can AI Green Itself?
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Key Impact Points:
- AI and accelerated computing are critical in optimizing energy use and reducing emissions across industries.
- Accelerated computing platforms can be up to 20× more energy-efficient than traditional CPU-only systems.
- AI innovations are essential tools for tackling climate change and achieving sustainable outcomes.
AI and accelerated computing are transforming industries by driving energy efficiency and offering innovative solutions to global sustainability challenges, according to Joshua Parker, senior director of corporate sustainability at NVIDIA, in a recent episode of NVIDIA’s AI Podcast.
Why it matters: AI isn’t just about building smarter machines—it’s about building a greener world. From optimizing energy use to reducing emissions, AI helps industries tackle some of the toughest environmental challenges, including its own energy consumption.
The details:
Parker, a seasoned sustainability professional with a background in law and engineering, emphasizes that AI systems can significantly reduce energy consumption, often in surprising ways.
“AI still accounts for a tiny, tiny fraction of overall energy consumption globally,” Parker said. “Yet, the potential for AI to optimize energy use is vast.”
Accelerated computing platforms that combine GPUs and CPUs are designed to handle complex computations quickly and efficiently.
“The change in efficiency is really, really dramatic,” Parker emphasized. “If you compare the energy efficiency for AI inference from eight years ago until today, [it’s] 45,000 times more energy efficient.”
These systems can be up to 20× more energy-efficient than traditional CPU-only systems for AI inference and training.
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Tackling climate change:
AI and accelerated computing are game-changers in weather and climate modeling.
“AI and accelerated computing in general are game-changers when it comes to weather and climate modeling and simulation,” Parker noted.
- AI-enhanced weather forecasting allows industries and governments to better prepare for climate-related events like hurricanes or floods, reducing wasted resources and minimizing damage.
- Digital twins—virtual models of physical environments—enable companies to optimize energy consumption in real time without costly physical changes. In one case, a company achieved a 10% reduction in energy use using a digital twin.
On data centers:
As AI grows, so does the demand for computing power. Data centers can become part of the sustainability solution through innovations like direct-to-chip liquid cooling.
“Our recommended design for the data centers for our new B200 chip is focused all on direct-to-chip liquid cooling,” Parker explained. “By cooling directly at the chip level, this method saves energy, helping data centers stay cool without guzzling power.”
Looking ahead:
The future of data centers depends on designing for energy efficiency from the ground up.
“The compute density is so high that it makes more sense to invest in the cooling because you’re getting so much more compute for that same single direct-to-chip cooling element,” Parker said.
The bottom line:
AI is not just a tool for optimizing systems—it’s a driver of sustainable innovation, including addressing its own sustainability challenges.
“AI, I firmly believe, is going to be the best tool that we’ve ever seen to help us achieve more sustainability and more sustainable outcomes,” Parker stated.