Strict privacy rules bottlenecked AI rollouts. Now, a hybrid self-service platform dynamically routes tasks by data sensitivity.
A major U.S. higher education institution operates one of the country's most powerful university supercomputers to support research and education for tens of thousands of students and staff.
The institution needed to deploy customized learning assistants and foundation models across campus but faced strict requirements around data privacy...
“When I talk to presidents and provosts around the country, I give them a very clear message: If you don’t do AI on your campus, the students are going to vote with their feet and go somewhere where they can learn this stuff, because they know it’s important to their futures.”
Public land-grant research university offering over 300 degree programs.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
University of Florida's Self-service computing portal is part of this use case:
Related implementations across industries and use cases
Robotics labs capped at 6–7 training runs/day on desktop GPUs. Shared campus compute lifted that to 100+ runs daily.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
150+ siloed AI projects fragmented the campus. Standardizing on Copilot empowered students and staff to securely automate daily workflows.
Robotics labs capped at 6–7 training runs/day on desktop GPUs. Shared campus compute lifted that to 100+ runs daily.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Deal data scattered across systems kept sellers from districts. AI unified it—leadership moved from retrospective reports to live intel.
Consultative selling couldn't scale. AI now tags thousands of meeting recordings to targeted skills so managers can actively coach.
Scaling AI access across a 100,000-employee workforce risked system overloads. An auto-scaling platform launched with zero downtime.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
Strict privacy rules bottlenecked AI rollouts. Now, a hybrid self-service platform dynamically routes tasks by data sensitivity.
A major U.S. higher education institution operates one of the country's most powerful university supercomputers to support research and education for tens of thousands of students and staff.
The institution needed to deploy customized learning assistants and foundation models across campus but faced strict requirements around data privacy...
“When I talk to presidents and provosts around the country, I give them a very clear message: If you don’t do AI on your campus, the students are going to vote with their feet and go somewhere where they can learn this stuff, because they know it’s important to their futures.”
Public land-grant research university offering over 300 degree programs.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
University of Florida's Self-service computing portal is part of this use case:
Related implementations across industries and use cases
Robotics labs capped at 6–7 training runs/day on desktop GPUs. Shared campus compute lifted that to 100+ runs daily.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
150+ siloed AI projects fragmented the campus. Standardizing on Copilot empowered students and staff to securely automate daily workflows.
Robotics labs capped at 6–7 training runs/day on desktop GPUs. Shared campus compute lifted that to 100+ runs daily.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Deal data scattered across systems kept sellers from districts. AI unified it—leadership moved from retrospective reports to live intel.
Consultative selling couldn't scale. AI now tags thousands of meeting recordings to targeted skills so managers can actively coach.
Scaling AI access across a 100,000-employee workforce risked system overloads. An auto-scaling platform launched with zero downtime.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.