Case Study: Leading Global Bank achieves 10x more data scientists per GPU and faster time-to-market with NVIDIA Run:ai

How a leading global bank scaled AI efficiently across regions and teams with Run.ai

Leading Global Bank, a multi‑trillion‑dollar financial institution with hundreds of data scientists and multiple on‑prem GPU clusters, struggled with low GPU utilization, resource contention, and projects consuming whole GPUs for small experiments. The bank needed enterprise‑wide consistency, governance, and smarter allocation of compute to align AI investments with business priorities, so it turned to NVIDIA Run:ai and its Run:ai platform to address these challenges.

NVIDIA Run:ai implemented a workload‑aware scheduler and pooling layer that enabled fractional GPU usage, policy‑based, business‑priority allocation, and seamless integration with the bank’s data science tools and compliance controls. The Run:ai platform let 10X more data scientists work on the same number of GPUs, increased utilization, reduced waste, accelerated iteration cycles and time to market, and allowed dozens of AI/ML projects to be resourced according to business priorities for higher ROI.