Compute limits capped labs at 1–2 targets/year. A shared 760-GPU cluster let one team screen 10M molecules in a week.
One of the largest research universities in the United States, anchoring a system of seven institutions with more than $1 billion in annual research expenditures across disciplines from drug discovery to climate modeling and engineering.
For years, researchers wrote grants to fund isolated clusters, waited months for national facility allocations, or queued on aging hardware that...
“I was watching years’ worth of struggle tick off in minutes. I had no problems. It was a dream.”
Public land-grant research university.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Texas A&M University's Drug discovery 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.
Siloed spreadsheets held back research for years. AI now scans 100M+ data points to guide wound care, cutting analysis cycles to weeks.
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Consultative selling couldn't scale. AI now tags thousands of meeting recordings to targeted skills so managers can actively coach.
Ops teams combed Gong, Zendesk, Slack, and Salesforce to answer routine questions. Now one query surfaces cited answers.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Compute limits capped labs at 1–2 targets/year. A shared 760-GPU cluster let one team screen 10M molecules in a week.
One of the largest research universities in the United States, anchoring a system of seven institutions with more than $1 billion in annual research expenditures across disciplines from drug discovery to climate modeling and engineering.
For years, researchers wrote grants to fund isolated clusters, waited months for national facility allocations, or queued on aging hardware that...
“I was watching years’ worth of struggle tick off in minutes. I had no problems. It was a dream.”
Public land-grant research university.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Texas A&M University's Drug discovery 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.
Siloed spreadsheets held back research for years. AI now scans 100M+ data points to guide wound care, cutting analysis cycles to weeks.
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Consultative selling couldn't scale. AI now tags thousands of meeting recordings to targeted skills so managers can actively coach.
Ops teams combed Gong, Zendesk, Slack, and Salesforce to answer routine questions. Now one query surfaces cited answers.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.