Powering the Next Frontier of Academic Research at The Texas A&M University System

About The Texas A&M University System 

The Texas A&M University System unites 12 universities, a comprehensive health science center and eight state agencies to address complex research challenges. Supporting nearly 175,000 students, faculty and staff, the system invests approximately $1.6 billion annually in research, advancing scientific discovery, economic development and technological leadership across Texas and beyond. 

Situation

The Texas A&M University System operates at a scale few academic institutions can match, driving high-impact research across engineering, health sciences, agriculture, data science and national security. As AI adoption expanded across the A&M System's universities and state agencies, researchers increasingly needed access to computing resources capable of supporting data-intensive and model-driven work. 

At the same time, A&M System technology leaders had to account for cross-institution requirements around security, data governance and responsible use. While they could add compute capacity incrementally, that approach would fall short of supporting AI-driven research needs as demand continues to grow in the coming years.

To position researchers for long-term success, the A&M System needed to evolve how AI infrastructure and governance were implemented. This included aligning approaches to data governance across campuses, establishing an architecture that met academic and regulatory requirements, and coordinating infrastructure deployments across multiple universities, vendors and facilities.

Dr. Vince Kellen, Chief Information Officer, The Texas A&M University System

Solution

To meet the growing demands for AI research, the A&M System established VISION, an NVIDIA DGX SuperPOD™ that provides high-performance supercomputing for GPU-based workloads and represents one of the largest supercomputing environments in higher education.

Bringing VISION into production required an infrastructure effort that demanded precision, coordination and validation at scale. The A&M System needed a technology partner that could align AI readiness, governance and security considerations with a complex, systemwide rollout.

To achieve this, WWT provided:

• AI readiness and planning to translate systemwide goals into a phased infrastructure rollout.
• The AI Readiness Model for Operational Resilience (ARMOR), a vendor-agnostic solution delivered by WWT that leverages a jointly developed framework with NVIDIA.
• Large-scale AI infrastructure delivery, supporting deployment of the VISION high-performance computing environment.
• Systemwide coordination and execution support, aligning engineering, facilities and stakeholders throughout rollout.
• AI literacy training, preparing faculty, staff and administrators to use AI responsibly across the A&M System.

Ed Evans, Associate Vice President for IT Enterprise Operations, Texas A&M University

By the numbers

Outcomes and benefits

• Systemwide access to advanced AI computing: VISION triples the A&M System's supercomputing capacity, giving researchers across its institutions consistent access to the compute power needed to pursue research initiatives in areas including machine learning, generative AI applications, model training, image processing, graphics rendering, scientific simulations, robotics enablement and autonomous systems.
• Capacity to support national-scale research: VISION was designed to support the types of workloads required for national-scale research initiatives. The platform positions the A&M System as a leader in supercomputing, including participation in benchmarks such as the TOP500 list.
• Secure, governed access across institutions: With governance and security embedded into the platform through the ARMOR framework, the A&M System can provide researchers access to shared resources while maintaining clear controls around data handling, access and oversight.
• Organizational readiness for AI adoption: VISION is paired with an AI literacy learning path that helps faculty, staff and administrators build shared understanding around AI concepts, responsible use and security expectations. This prepares the A&M System to not only run AI workloads, but to govern and expand them responsibly.
• A scalable model for future growth: VISION establishes a repeatable model for expanding AI infrastructure over time. As demand grows, the A&M System is positioned to scale capacity, onboard new workloads and adapt governance without rearchitecting the environment.

Dr. Joe Elabd, Vice Chancellor for Research, The Texas A&M University System

Areas of Expertise

• AI readiness and training
• Infrastructure deployment
• Secure architecture design
• High-performance computing
• Data governance strategy
• Rack integration and configuration
• Vendor coordination
• Logistics and material handling

How we did it         

With 35 years of experience helping the world's largest private and public organizations, we've learned transformation and IT modernization thrive in the space between:  

Our deep domain expertise cuts across business and technology. Our ability to extensively test solutions and deploy them at scale allows us to both advise and execute, creating new realities for our clients. 

Here's how we did it for The Texas A&M University System:

We planned for AI at scale

We started by working with the A&M System's technology leaders to define infrastructure requirements, align governance and security considerations, and map dependencies across campuses and state agencies. This planning phase established AI readiness processes, compliance checkpoints and safe experimentation frameworks, while also accounting for power, cooling and space constraints associated with high-density computing and future expansion.

We integrated security and governance from the start

Because VISION was designed to serve multiple universities and state agencies, security and governance needed to be addressed early in the planning process. Our cybersecurity specialists worked with technology leaders to co-develop ARMOR, a framework for applying security and governance requirements to AI infrastructure deployment. This work focused on defining access approaches, data handling expectations and operational guardrails. ARMOR was then used to inform deployment decisions, align infrastructure with institutional policies and regulatory considerations, and support safe experimentation as AI use expands across the A&M System.

Danny Miller, Chief Information Security Officer, The Texas A&M University System

We took the guesswork out of infrastructure deployment

To reduce deployment risk and streamline system turn-up, we staged, configured and validated infrastructure prior to delivery. This included preparing 48 racks with 95 NVIDIA DGX systems      , each undergoing logical testing, firmware updates, power balancing, labeling and system validation at our North American Integration Center before shipment. It also required coordination with Vertiv for rack infrastructure and DDN for high-performance storage. Throughout the process, standardized documentation enabled consistency and repeatability across the environment.

We kept everyone on the same page 

Recognizing the complexity of coordinating delivery across engineering, logistics, facilities and A&M System stakeholders, we established weekly planning cycles to align schedules and manage dependencies. This coordination allowed teams to accommodate hardware substitutions and changes in delivery sequencing without disrupting timelines. It also helped manage practical constraints such as dock availability and on-site readiness.

We prepared people alongside systems

In parallel with infrastructure deployment, we supported the design and rollout of an AI literacy learning path across the A&M System. Training was conducted alongside system turn-up and focused on building a shared understanding of core AI concepts, responsible use principles, and data privacy and security expectations for faculty, staff and administrators.

How can we help you?

If your organization is exploring or accelerating AI adoption, we can help you:

• Assess your current readiness for AI and digital transformation to identify and address gaps in scalability, performance, compliance or governance.
• Design secure, high-performance computing environments for AI experimentation, data science and cross-functional collaboration.
• Develop strategic roadmaps to implement or enhance infrastructure models that align with your organization's goals.
• Streamline deployment through end-to-end integration services — from planning and staging to configuration and delivery.