TI and NVIDIA Partner to Power the Future of AI Data Centers This innovation could transform how data centers are built and maintained, making it easier to meet the high energy demands of AI systems without increasing the physical footprint or complexity of the infrastructure.
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In an exciting move for the tech world, Texas Instruments (TI) has announced a collaboration with NVIDIA to develop advanced power management and sensing technologies for 800V high-voltage direct current (HVDC) power systems.
This next-generation power architecture is designed to support the growing demands of AI data centers, which are rapidly expanding in both size and complexity.
As artificial intelligence becomes more powerful, the amount of electricity needed to run a single data center rack is expected to grow from 100kW today to over 1MW in the near future. This presents a major challenge. Traditional 48V systems simply can't keep up—they would need nearly 450 pounds of copper just to deliver 1MW to a rack, making the setup bulky and unsustainable.
That's where TI and NVIDIA come in. Their new 800V HVDC system promises to deliver power more efficiently, compactly, and scalably. This innovation could transform how data centers are built and maintained, making it easier to meet the high energy demands of AI systems without increasing the physical footprint or complexity of the infrastructure.
"A paradigm shift is happening right in front of our eyes," said Jeffrey Morroni, TI Fellow and director of power management R&D at Kilby Labs. "AI data centers are pushing power limits to levels we never imagined. TI's expertise in power conversion, combined with NVIDIA's AI leadership, is helping make 800V systems a reality."
Gabriele Gorla, VP of System Engineering at NVIDIA, added, "Semiconductor power systems are crucial to building high-performance AI infrastructure. By working with TI, we're creating an architecture that can support the next generation of large-scale AI data centers with greater efficiency."
This partnership is a significant step forward in enabling the future of AI computing—where performance, power, and practicality must go hand in hand.
