Warrendale-based Mitsubishi Electric introduces Chip-to-Grid blueprints for AI factories

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Mitsubishi Electric Power Products, based in Warrendale, PA, launched new Chip-to-Grid Reference Designs as an integrated blueprint to support AI factories built with NVIDIA Vera Rubin chips.

The reference designs provide scalable architecture built around 250MW deployment blocks and are designed for hyperscale, neocloud, and colocation operators across the country. The new designs will help customers accelerate AI campus development, while maximizing efficiency and scaling capacity for future growth.

“The race to build the next generation of AI infrastructure is driving one of the most significant transformations of our time,” Tricia Breeger, President and CEO of Mitsubishi Electric Power Products, Inc., said. “As AI demand accelerates, success depends on more than computing power. It requires resilient energy systems and scalable infrastructure that can grow from today’s deployments to tomorrow’s gigawatt-scale campuses. Our Chip-to-Grid Reference Designs help customers deploy AI infrastructure faster and scale with confidence.”

As generative AI and accelerated computing drive demand for AI factory capacity, the company said, access to power is one of the industry’s most significant constraints. To address this challenge, MEPPI’s designs will integrate into one design utility interconnection, on-site generation, battery energy storage, electrical distribution, advanced cooling, and facility controls.

Officials said the design aligns with NVIDIA MGX rack-scale accelerated computing platforms, and future high-density data center platforms, as well as the NVIDIA Vera Rubin DSX AI Factory Reference Design infrastructure provisioning strategy.

“AI factories require compute, power and cooling to work together as one system,” said Vladimir Troy, Vice President of AI Infrastructure at NVIDIA, said. “Mitsubishi Electric’s Chip-to-Grid Reference Designs give operators a repeatable blueprint for deploying NVIDIA Vera Rubin infrastructure and scaling toward gigawatt-scale AI campuses.”