Thai Le, faculty member in the Department of Computer Science at Indiana University’s Luddy School of Informatics, Computing and Engineering, has been leveraging NSF ACCESS allocations on Jetstream2 to transform how students learn about trustworthy AI research. The high-performance resource has become instrumental in Le’s AI courses for both undergraduate and graduate students. 

“We’re using our ACCESS allocations to conduct dynamic in-class lab experiments on Jetstream2’s computing nodes, which feature high-performing GPUs,” Le said. “Modern AI models, particularly Large Language Models (LLMs), contain billions of parameters and require GPUs with substantial memory – without ACCESS allocation support, demonstrating these sophisticated models within our 90-minute lecture sessions would be impossible.”

As the course progresses to its second half, students utilize these same ACCESS allocations on Jetstream2 for their individual and team projects, many focusing on cutting-edge LLM research. This hands-on experience with high-performance computing resources gives students invaluable practical skills in working with advanced AI systems.

“Thanks to NSF ACCESS allocations, we’re equipping both undergraduate and graduate students with the practical knowledge of how to harness high-performance computing for generative AI and LLM research – skills that will be essential for the next generation of AI researchers and practitioners,” Le said.

Resource Provider Institution(s): Indiana University (IU)
Resources Used: Jetstream2
Affiliations: Luddy School of Informatics, Computing and Engineering at Indiana University
Funding Agency: NSF
Grant or Allocation Number(s): CIS240570

The science story featured here was enabled by the U.S. National Science Foundation’s ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.