A Supercomputer for National Security Education
NVIDIA founder and CEO Jensen Huang visited the Naval Postgraduate School in Monterey, California, to commission a DGX GB300 supercomputer, bringing one of the world's most powerful AI platforms fully online for the U.S. military's flagship graduate university. The system gives NPS's more than 1,500 in-resident students and 600 faculty on-premises access to large-scale AI computing for applications spanning weather prediction, cybersecurity, and disaster resilience planning.
The commissioning, part of the school's three-day Converge @ NPS event, anchors an NVIDIA AI Technology Center on the Monterey campus dedicated to AI research and graduate instruction.
Modernizing Military Education
Admiral Samuel Paparo, commander of the U.S. Pacific Command, framed the installation in strategic terms. "As we modernize our technology, we must also modernize how we educate our leaders," he said. "Access to advanced computing capability means NPS students and faculty understand the opportunities and responsibilities that come with these technologies."
NPS educates active-duty officers and international partners across disciplines from space operations to ocean science, with each program centered on applied research with direct relevance to real-world military and defense problems. The DGX GB300 adds new technical depth: the ability to train foundation models in-house, run high-fidelity simulations at scale, and develop AI tools with immediate operational applicability.
AI Across the Curriculum
NVIDIA is providing instructor toolkits to NPS faculty to weave AI through graduate curricula across departments. This reflects a broader recognition that future military leaders will command in AI-enabled environments where information moves at speeds that compress traditional decision cycles.
"Many of you will command in AI-enabled environments," Admiral Paparo told attendees. "Our advantage will come from leaders, like you, who can use the technology to see, understand, decide and act faster while exercising the judgment, experience and leadership that machines cannot provide. That education is happening here at NPS."
Research Applications
NPS runs regular hackathons that have produced advances in autonomy, ocean research, and operations planning. Researchers are using AI to model sea conditions, predict atmospheric changes, and build digital twins of complex operational environments. The DGX GB300's scale enables these projects to move from proof-of-concept to serious computational studies.
Jensen Huang emphasized accessibility, telling attendees that modern AI computers are designed to be approachable even for those without computer science backgrounds. "The most important advice that I would give to someone is to engage the technology," he said. "It's not as hard as you think it is, and the reason for that is because, on first principles, technology is supposed to get smarter and smarter over time."
Infrastructure Partners
The system deployment involved multiple infrastructure partners. DDN contributed high-performance data infrastructure for demanding AI workloads. NetApp provided a unified data platform for secure data access across edge, core, and cloud environments. Vertiv supplied racks, cooling, power infrastructure, and commissioning support.
Context: Military AI and Domestic Infrastructure
The NPS installation is part of a broader pattern of AI infrastructure investment with national security implications. As the U.S. competes with China in AI development, access to frontier computing for defense research and officer education is increasingly viewed as a strategic priority. The DGX GB300 gives NPS capabilities comparable to leading civilian research institutions, ensuring that military graduates enter service with hands-on experience in training and deploying large AI models.
The on-premises nature of the system is also significant. For classified and sensitive defense applications, cloud-based AI training is often not an option. A dedicated, secure supercomputer on campus allows NPS to work with restricted data and develop tools that can transition directly to operational use without the security complications of commercial cloud environments.