By: Dan Fuentes, Senior Vice President of Enterprise Sales
The computing needs of colleges and universities have evolved dramatically over the past decade. Today, the rise of artificial intelligence has only accelerated that change.
In the past, traditional academic computing once centered on simulations, data storage, and basic research models. Yet today’s higher education institutions require massive, flexible high-performance computing (HPC) resources to fuel AI-driven discovery. From training complex machine learning models to running large-scale simulations and analyzing unprecedented volumes of data, modern AI research demands HPC infrastructure, deep collaboration, and innovation on a scale few individual institutions can manage alone.
For example, earlier this year, Texas A&M announced a $45 million investment in NVIDIA’s DGX SuperPOD system. This move is expected to triple its supercomputing capacity to support AI research. This announcement follows similar moves by leading higher education institutions such as the New Jersey Institute of Technology (NJIT), the University of Maryland, and Georgia Tech. In the case of NJIT, the university expects its AI research to be one-third of its total research budget.
Several recent research findings further highlight the trend of AI and its implications for colleges and universities:
AI is clearly reshaping academic priorities at every level. In a recent AI panel discussion at NJIT and sponsored by DataBank, NJIT President Teik C. Lim commented on the role it will play. “AI is creating new and extensive opportunities for innovation and knowledge creation,” he said. “AI is going to have arguably the greatest effect on the creation of knowledge, goods, and services since the invention of the internet and the smartphone.”
Higher education institutions that fail to invest in AI risk falling behind, both in research and their overall reputation.
Colleges and universities are under growing pressure to embrace AI not only as a research tool but as a strategic asset for attracting top talent and funding. Institutions that can offer cutting-edge AI environments are far more likely to draw leading researchers, high-achieving students, and major grant opportunities.
In a competitive academic landscape, a university’s AI capabilities are increasingly tied to its reputation. The more advanced and “AI-ready” a campus becomes, the more it is viewed as a hub for innovation, discovery, and industry partnerships.
Beyond the technology itself, AI offers a critical business advantage for higher education. By cultivating a strong AI ecosystem, universities can secure larger research grants, generate valuable intellectual property, and foster partnerships with tech companies, government agencies, and private investors.
As demand for AI-powered technology and workloads skyrockets, colleges and universities face a critical decision. Should they attempt to build their own data center infrastructure, or should they partner with a colocation provider to meet their needs faster and more efficiently?
At first glance, building new or expanding existing facilities may seem appealing, especially for institutions that already own disparate data centers and server rooms. Yet, the reality is far more complex and costly.
Supporting today’s high-density AI workloads demands massive upgrades in cooling systems, power capacity, water usage, and ongoing management. Higher education institutions would need to invest in not only hardware but also in advanced infrastructure like water cooling, redundant generators, and specialized environmental controls. Beyond the expense, managing such a facility would pull valuable time, funding, and focus away from their core academic mission.
Recognizing these challenges, many institutions are turning to colocation providers like DataBank. By partnering with a colocation expert, universities can get to market faster, scale their AI capabilities more easily, and keep their internal teams focused on research, education, and innovation. Instead of sinking limited funding into brick-and-mortar facilities, they can direct more resources toward the compute power, talent, and research initiatives that attract top students, researchers, and grants.
In the case of NJIT, President Lim credits the collaborative partnership they’ve had with DataBank. “DataBank helped us create a modern data center that is essential to NJIT’s AI work and aspiration,” he said. “This partnership will significantly advance the university’s strengths in AI.”
Beyond just providing space, power, and cooling, colocation providers deliver another critical advantage: a wide range of interconnection options and capabilities. Today’s universities operate complex hybrid environments, with research workloads spanning their own campuses, public cloud providers, hyperscalers, and even other partner institutions. To succeed, they need fast, reliable, and flexible connectivity across all these platforms. This is something traditional on-campus data centers often struggle to support.
Colocation facilities are built to meet these demands. With carrier-neutral environments and access to multiple network providers, universities gain the flexibility to design the right mix of connections for their research and operational needs. Whether linking directly to a hyperscaler or securely transferring massive datasets between campuses, colocation makes it easier to build the connected foundation that AI research and academic collaboration require.
As AI reshapes the landscape of higher education, institutions face pivotal decisions on how to support the growing demand for AI-powered research and innovation. By partnering with colocation providers like DataBank, universities can accelerate their AI capabilities, secure critical funding, and remain competitive in an increasingly tech-driven academic environment.
Dan Fuentes is the senior vice president of Enterprise Sales at DataBank, where he leads sales strategies and fosters key client relationships to support growth in enterprise IT solutions. With over three decades of experience in technology and data infrastructure, Dan has a proven record in advancing sales initiatives and leading high-performance teams. He attributes his success to fostering a positive culture where his sales organization values all relationships, internally and with clients. Prior to joining DataBank, he held a senior leadership role at Zayo Group, and senior sales roles at Equinix, Sungard AS, Switch and Data & AT&T. He has specialized in IT transformation and scalable infrastructure solutions, consistently exceeding revenue targets and achieving the highest levels of client satisfaction. Dan’s industry knowledge and strategic insights play a pivotal role in expanding DataBank’s reach within the enterprise sector.
Discover how NJIT partnered with DataBank to future-proof its high-performance computing needs. By leveraging scalable colocation solutions, NJIT achieved cutting-edge performance, avoided costly infrastructure investments, and accelerated deployment timelines—empowering $160 million in annual research and cementing its role as a leader in technological innovation.
The University of Maryland, a world-class research institution with a strong commitment to solving global challenges, faced a critical issue in maintaining 24x7 power and cooling for its on-campus data centers supporting High-Performance Computing (HPC). They found a reliable partner in DataBank.
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