A top-ranked private research university located within a major metropolitan hub recently launched a campus-wide AI initiative to expand the use of artificial intelligence across academic and professional disciplines. Because the ambitious plan introduced new demands for data-intensive, high-performance computing (HPC) workloads, the school needed to know whether its existing data center infrastructure could support this next phase of growth without significant capital commitments.
VALIDATING INFRASTRUCTURE FOR WIDESPREAD AI ADOPTION
Early HPC research activity had already begun to test the university’s data center. Now as GPU-enabled workloads were rapidly accelerating, leadership anticipated the need for incremental upgrades to power density, cooling, and network performance. The overriding concern was whether systems could scale reliably and cost-efficiently within the current environment.
To move forward with confidence, they needed a clear understanding of how the data center’s electrical, mechanical, and network infrastructure would perform under sustained AI and HPC loads. Just as critical, the assessment needed to account for perspectives across academic, IT, and facilities teams to ensure infrastructure decisions were aligned with both research priorities and operational realities.
DETAILED ASSESSMENT ENSIRES FUTURE-READY DATA CENTER
The LEDG team took a multifaceted approach in assessing the school’s infrastructure. Through rigorous onsite visits that included gathering requirements from stakeholders as well as a detailed review of existing systems’ documentation, LEDG got a full picture of the school’s desired future state and what would be needed to achieve its goals, including a breakout of associated costs and risks.
LEDG delivered a detailed assessment of the infrastructure and data center, which found that the main components—electrical utility size, emergency generator size, UPS and UPS bypass distribution, chiller capacity—could support the initiative without huge capital expense.
More importantly, the university is now able to:
• Align computing priorities across key stakeholders (academic, IT, facilities teams)
• Understand current infrastructure capabilities—and identify what’s needed next
• Cost-effectively scale the data center to support growing AI/HPC workloads
• Plan and budget for phased incremental upgrades
• Make informed infrastructure decisions as the AI initiative expands
With a clear understanding of its infrastructure readiness, the university now has a confident, actionable plan to support the AI-intensive research without overextending its existing data center environment.
"With our unique, in-depth approach to evaluating campus infrastructure and AI computing, the university is now confident they can support today's requirements and emerging technology innovations."
Bill Crane, Director – Design-Build Services
Leading Edge Design Group