Energy Department announces two AI supercomputers under new public-private partnership

Chris Wright, Secretary of the U.S. Department of Energy
Chris Wright, Secretary of the U.S. Department of Energy
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The U.S. Department of Energy (DOE) has announced the deployment of two new artificial intelligence (AI) supercomputers at Oak Ridge National Laboratory (ORNL). The initiative includes a new public-private partnership model aimed at speeding up the construction and operation of advanced computing systems.

One of the supercomputers, called Lux, will be powered by AMD Instinct MI355X GPUs, AMD EPYC CPUs, and AMD Pensando networking technology. Lux is scheduled for deployment in early 2026 and is expected to enhance DOE’s AI capacity for work in areas such as fusion energy, materials discovery, quantum science, manufacturing, and modernization of the electric grid. According to DOE officials, Lux will offer a secure and efficient AI software stack designed to strengthen innovation in the United States.

“Winning the AI race requires new and creative partnerships that will bring together the brightest minds and industries American technology and science has to offer,” said U.S. Secretary of Energy Chris Wright. “That’s why the Trump administration is announcing the first example of a new commonsense approach to computing partnerships with Lux. We are also announcing, as part of a competitive procurement process, Discovery. Working with AMD and HPE, we’re bringing new capacity online faster than ever before, turning shared innovation into national strength, and proving that America leads when private-public partners build together.”

AMD chair and CEO Dr. Lisa Su commented on the collaboration: “We are proud and honored to partner with the U.S. Department of Energy and Secretary Wright to accelerate America’s AI compute infrastructure. This partnership exemplifies public-private collaboration at its best. With Discovery and Lux, we are delivering leadership compute systems that combine performance and energy efficiency to advance America’s research priorities and strengthen U.S. leadership in AI, energy, and national security.”

DOE’s new partnership model allows both public funding from DOE and investments from private partners like AMD and Hewlett Packard Enterprise (HPE), reducing setup time for supercomputers from years to months through co-investment strategies.

The second supercomputer system announced is Discovery—a system developed by HPE using next-generation AMD processors—set to arrive in 2028. Discovery will feature HPE Cray Supercomputing GX5000 architecture based on upcoming AMD EPYC “Venice” processors and AMD Instinct MI430X GPUs. This system aims to surpass Frontier—the world’s second-largest supercomputer currently located at ORNL—in performance capabilities.

Discovery is expected to facilitate rapid data analysis for scientific problems across fields including medicine, energy research, cybersecurity initiatives, and manufacturing advancements.

“We are proud to build on our strong U.S. public-private partnership with the Department of Energy, Oak Ridge National Laboratory and AMD that first began when we debuted the Frontier exascale supercomputer and broke a significant computing speed barrier,” said HPE president and CEO Antonio Neri. “Together, we will continue to strengthen U.S. national leadership in the era of AI and accelerate scientific breakthroughs and innovation with Discovery and Lux.”

Stephen Streiffer, Director at ORNL added: “The Discovery system will drive scientific innovation faster and farther than ever before. Oak Ridge’s leadership in supercomputing has transformed how researchers solve problems. With Discovery and Lux, we’re accelerating the pace of Gold Standard Science at a scale that secures America’s leadership in an increasingly competitive world.”

According to DOE officials, more than $1 billion in combined investment from public funds as well as contributions from private companies supports this initiative.

Lux and Discovery aim not only to boost computational power but also enhance secure data transfer across sites while integrating modeling with experimental research efforts.



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