Renesas backs Nvidia’s 800V power architecture with GaN solutions

By Setform

Renesas’ GaN solutions can sustain efficient, dense DC/DC power conversion with operating voltages ranging from 48V to 400V

Renesas Electronics Corporation, a supplier of advanced semiconductor solutions, is supporting efficient power conversion and distribution for Nvidia's 800 Volt Direct Current power architecture, accelerating intelligent, faster AI infrastructure
 
As GPU-driven AI workloads intensify and data centre power consumption rises into the multi-hundred-megawatt range, modern data centres are adopting power architectures that are energy-optimised and scalable.

Wide-bandgap semiconductors, such as GaN FET switches, are becoming key solutions due to faster switching, decreased energy losses, and enhanced thermal management. GaN power devices also enable the development of 800V direct current buses within racks, reducing distribution losses and the necessity for large bus bars, while supporting the reuse of 48V components using DC/DC step-down converters.
 
Renesas' GaN-based power solutions are suitable for the task, sustaining efficient, dense DC/DC power conversion with operating voltages ranging from 48V to 400V, with the option to stack to 800V.
 
Based on the LLC Direct Current Transformer topology, these converters can reach up to 98% efficiency. For the AC/DC front-end, Renesas utilises bi-directional GaN switches to facilitate rectifier designs and improve power density. 
 
Zaher Baidas, senior vice president and general manager of Power at Renesas, said, “AI is transforming industries at an unprecedented pace, and the power infrastructure must evolve just as quickly to meet the explosive power demands. Renesas is helping power the future of AI with high-density energy solutions built for scale, supported by our full portfolio of GaN FETs, MOSFETs, controllers and drivers. These innovations will deliver performance and efficiency, with the scalability required for future growth.” 
 
Renesas has published a white paper exploring the topology of its devices supporting 800V power distribution in AI infrastructure.

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