Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
A research university whose seven schools span AI, materials science, neuroscience, medical imaging, and climate modeling, with an existing shared HPC cluster serving 8,000+ researchers and 1,000+ GPUs.
The existing cluster was a general shared environment, not optimized for large-scale multi-node GPU training. Researchers scaling compute-intensive...
“Stanford's research computing ecosystem empowers world-class discovery through specialized services designed for every scale of computation.”
Private research university offering higher education and academic research.
Supported the deployment and ongoing integration of Marlowe into Stanford's compute ecosystem.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Stanford University's Research computing is part of this use case:
Related implementations across industries and use cases
Compute limits capped labs at 1–2 targets/year. A shared 760-GPU cluster let one team screen 10M molecules in a week.
Researchers had just 30-60 minutes to warn of severe hailstorms. Now, an AI radar model generates high-resolution forecasts in minutes.
Researchers lost months debugging network failures. Bare-metal clusters ended the crashes and enabled custom orchestration.
Strict privacy rules bottlenecked AI rollouts. Now, a hybrid self-service platform dynamically routes tasks by data sensitivity.
Researchers lost months debugging network failures. Bare-metal clusters ended the crashes and enabled custom orchestration.
Consultative selling couldn't scale. AI now tags thousands of meeting recordings to targeted skills so managers can actively coach.
Agents juggling 8+ tools struggled to keep pace. Now, AI handles 35% of chats, letting the team resolve complex issues 58% faster.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
A research university whose seven schools span AI, materials science, neuroscience, medical imaging, and climate modeling, with an existing shared HPC cluster serving 8,000+ researchers and 1,000+ GPUs.
The existing cluster was a general shared environment, not optimized for large-scale multi-node GPU training. Researchers scaling compute-intensive...
“Stanford's research computing ecosystem empowers world-class discovery through specialized services designed for every scale of computation.”
Private research university offering higher education and academic research.
Supported the deployment and ongoing integration of Marlowe into Stanford's compute ecosystem.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Stanford University's Research computing is part of this use case:
Related implementations across industries and use cases
Compute limits capped labs at 1–2 targets/year. A shared 760-GPU cluster let one team screen 10M molecules in a week.
Researchers had just 30-60 minutes to warn of severe hailstorms. Now, an AI radar model generates high-resolution forecasts in minutes.
Researchers lost months debugging network failures. Bare-metal clusters ended the crashes and enabled custom orchestration.
Strict privacy rules bottlenecked AI rollouts. Now, a hybrid self-service platform dynamically routes tasks by data sensitivity.
Researchers lost months debugging network failures. Bare-metal clusters ended the crashes and enabled custom orchestration.
Consultative selling couldn't scale. AI now tags thousands of meeting recordings to targeted skills so managers can actively coach.
Agents juggling 8+ tools struggled to keep pace. Now, AI handles 35% of chats, letting the team resolve complex issues 58% faster.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.