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UT Eclipses 5,000 GPUs To Increase Dominance in Open-Source AI, Strengthen Nation’s Computing Power

Amid the private sector’s race to lead artificial intelligence innovation, The University of Texas at Austin has strengthened its lead in academic computing power and dominance in computing power for public, open-source AI. UT has acquired high-performance Dell PowerEdge servers and NVIDIA AI infrastructure powered by more than 4,000 NVIDIA Blackwell architecture graphic processing units (GPUs), the most powerful GPUs in production to date.

The new infrastructure is a game-changer for the University, expanding its research and development capabilities in agentic and generative AI while opening the door to more society-changing discoveries that support America’s technological dominance. The NVIDIA GB200 systems and NVIDIA Vera CPU servers will be installed as part of Horizon, the largest academic supercomputer in the nation, which goes online next year at UT’s Texas Advanced Computing Center (TACC). The National Science Foundation (NSF) is funding Horizon through its Leadership Class Computing Facility program to revolutionize U.S. computational research.

UT has the most AI computing power in academia. In total, the University has amassed more than 5,000 advanced NVIDIA GPUs across its academic and research facilities. The University has the computing power to produce open-source large language models — which power most modern AI applications — that rival any other public institution. Open-source computing is nonproprietary and serves as the backbone for publicly driven research. Unlike private sector models, it can be fine-tuned to support research in the public interest, producing discoveries that offer profound benefits to society in such areas as health care, drug development, materials and national security.

One Giant Leap for AI Physics: NVIDIA Apollo Unveiled as Open Model Family for Scientific Simulation

NVIDIA Apollo will provide pretrained checkpoints and reference workflows for training, inference and benchmarking, allowing developers to integrate and customize the models for their specific needs.

Industry Leaders Tap Into NVIDIA AI Physics

Applied Materials, Cadence, LAM Research Corp., Luminary Cloud, KLA, PhysicsX, Rescale, Siemens and Synopsys are among the industry leaders that intend to train, fine-tune and deploy their AI technologies using the new open models. These companies are already using NVIDIA AI models and infrastructure to bolster their applications.

Interpretable AI reveals key atomic traits for efficient hydrogen storage in metal hydrides

Hydrogen fuels represent a clean energy option, but a major hurdle in making its use more mainstream is efficient storage. Hydrogen storage requires either extremely high-pressure tanks or extremely cold temperatures, which means that storage alone consumes a lot of energy. This is why metal hydrides, which can store hydrogen more efficiently, are such a promising option.

To help accurately predict performance metrics of materials, researchers at Tohoku University used a newly established data infrastructure: the Digital Hydrogen Platform (DigHyd). DigHyd integrates more than 5,000 meticulously curated experimental records from the literature, supported by an AI language model. The work is published in the journal Chemical Science.

Leveraging this extensive database, researchers systematically explored physically interpretable models and found that fundamental atomic features— , electronegativity, molar density, and ionic filling factor—emerge as key descriptors. Other researchers can use this as a tool for guiding their materials design process, without having to go through a lengthy trial-and-error process in the lab to search for .

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