Schneider Electric, a global leader in energy management and automation, has recently partnered with NVIDIA to advance the data center infrastructure and pioneer groundbreaking advancements in edge AI and digital twin technologies. This partnership aims to introduce reference designs for the first AI-enabled data centers that can be widely used by the public, which is expected to redefine the standard for AI deployment and operations in the data center ecosystem, marking an important milestone in the industry’s evolution.
In the first phase of this collaboration, Schneider Electric will introduce advanced data center reference designs, specifically designed for clusters with accelerated computing and data processing capabilities by NVIDIA. These clusters will be used for data processing, engineering simulation, electronic design automation, computer-aided drug design (CADD), and generative AI. The reference designs will provide a solid foundation for deploying NVIDIA’s compute and data processing acceleration platform in data centers, meeting the growing computing demands of AI workloads, while optimizing processing performance, scalability, and overall energy sustainability.
Moreover, Schneider Electric is focused on providing data center operators with the tools and resources needed to integrate new and emerging AI solutions into their infrastructure seamlessly. They aim to improve deployment efficiency and ensure stability for the operating life cycle of solutions. Datacenter partners, engineers, and leaders can leverage these reference designs as a basis for deploying high-density AI server systems in existing data centers and hubs, or a new data center fully optimized for AI clusters using a liquid cooling system.
In addition, Schneider Electric’s member company AVEVA will connect its digital twin platform to NVIDIA Omniverse, creating a unified environment for virtual simulation and virtual collaboration. This integration will make data center construction between design consultants, engineers, and stakeholders more seamless and easier, helping to speed up the design and implementation process of complex data systems, reducing product production time, and saving costs.