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It’s progress the wider community is starting to acknowledge, writes Dion Harris, head of data centre product marketing at Nvidia.
“When it comes to frontier applications like machine learning or deep learning, the performance of GPUs is an order of magnitude better than that of CPUs,” the report said. In manufacturing, Taiwan-based Wistron built a digital copy of a room where Nvidia DGX systems undergo thermal stress tests to improve operations at the site. It used Nvidia Omniverse, a platform for industrial digitisation, with a surrogate model, a version of AI that emulates simulations.
Using traditional methods, the work would have cost more than $400 million and taken up to six years. But with generative AI, Insilico hit the milestone for one-tenth of the cost in one-third of the time. Scientists and researchers worldwide depend on AI and accelerated computing to achieve high performance and efficiency.
The report notes as much as 90% of the cost — and all the efficiency gains — of running an AI model are in deploying it in applications after it’s trained. The reports also cited the potential of accelerated AI to fight climate change and promote sustainability. For its part, Nvidia is working with hundreds of startups employing AI to address climate issues. Nvidia also announced plans for Earth-2, expected to be the world’s most powerful AI supercomputer dedicated to climate science.Since its founding in 1993, Nvidia has worked on energy efficiency across all its products — GPUs, CPUs, DPUs, networks, systems and software, as well as platforms such as Omniverse.
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