The National Science Foundation has awarded a $35 million cooperative agreement over five years to the San Diego Supercomputer Center (SDSC) at UC San Diego to establish and run the NSF National AI Research Resource (NAIRR) Operations Center. SDSC will lead the effort in collaboration with the Texas Advanced Computing Center (TACC) at The University of Texas at Austin, creating the coordination layer that connects researchers, educators, and students with advanced computing, data, AI models, software, training, and technical expertise.
The Operations Center will serve as NAIRR's operational backbone, coordinating federal, private-sector, and academic resource providers while integrating computing, scientific data, AI models, and research tools. A unified national NAIRR portal will provide user support, training, documentation, outreach, and engagement. The center will also establish a user forum and feedback channels to ensure the resource evolves in response to community needs.
"The NAIRR-OC is an important step toward making advanced AI resources genuinely usable by researchers, educators and students across the country," said Frank Würthwein, SDSC Director and NAIRR-OC principal investigator and director. "By expanding access, we can enable ambitious ideas and discoveries that might otherwise remain out of reach."
Building on established infrastructure
UC San Diego and UT Austin bring complementary national expertise. UC San Diego is home to SDSC and leads the NSF-supported National Data Platform and National Research Platform. UT Austin is home to TACC, which leads the U.S. NSF Leadership-Class Computing Facility. The partnership reflects a strategy of integrating existing capabilities rather than building isolated infrastructure.
"For more than two decades, TACC has built and operated some of the nation's most advanced computing systems, data resources, and research tools that are usable through a unified experience," said Maytal Dahan, NAIRR-OC co-principal investigator and director of Advanced Computing Interfaces at TACC. The NAIRR portal will build on TACC's core Portal and Tapis frameworks to provide a single entry point to AI resources for the national research and education community.
The center will also work with federal and private-sector resource providers, strengthening a public-private partnership model that enables academic researchers to benefit from evolving AI capabilities while advancing independent, open, and exploratory research. For operations teams managing research infrastructure, the AI for Operations model here shows how centralized coordination can reduce fragmentation across distributed resources.
Workforce development and data integration
Training and workforce development are central to the center's mandate. The NAIRR-OC will pair resource access with clear processes for requesting and using services, accessible documentation, training programs, and user engagement channels. Data integration will draw from the NSF Integrated Data Systems and Services Program, helping researchers combine computing, AI tools, and data for reproducible scientific work across biomedicine, fusion energy, materials science, and public health.
"This program focuses on the basic AI and AI-enabled scientific research and innovation that lead to start-ups, products, and discovery that drive our economy," said SDSC Deputy Director Ashley Atkins, a NAIRR-OC co-principal investigator and deputy director. Atkins said the center aligns with the White House's Genesis mission to accelerate AI for Science & Research by giving the broader U.S. research community pathways to computing resources, data, training, and support.
The NAIRR Pilot, launched in January 2024, connected approximately 900 research teams and educators across all 50 states during its first 2.5 years. The Operations Center will build on that progress by establishing the sustained coordination needed for long-term national infrastructure.
Why this matters for operations professionals
The NAIRR-OC is a case study in operationalizing distributed infrastructure at national scale. It coordinates resource providers, standardizes access through a single portal, builds feedback loops through a user forum, and manages the transition from pilot to permanent operations. For professionals running technical operations, the model demonstrates how to move from fragmented pilots to coordinated service delivery without replacing existing assets. The five-year, $35 million scope also signals where federal AI infrastructure investment is heading - toward integration and accessibility rather than isolated compute builds.
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