Bristol Myers Squibb is purchasing an Nvidia DGX SuperPOD built on the chipmaker’s Vera Rubin architecture to support artificial intelligence use across its drug discovery and development operations.
The pharmaceutical company said it will be the first life sciences group to acquire a DGX SuperPOD based on Vera Rubin. Nvidia introduced the architecture earlier this year as the successor to its current generation of AI computing systems.
Expanding computing capacity
The new cluster will comprise eight DGX Vera Rubin NVL72 systems, with each rack-scale system combining Nvidia Vera central processing units and Rubin graphics processing units.
BMS will use the infrastructure to train proprietary models and run predictions across its research programmes. The system will support work involving compounds, proteins, and other scientific data.
Financial terms were not disclosed. The purchase expands BMS’s existing Nvidia infrastructure, which includes an older SuperPOD that company executives described as two or three generations behind Vera Rubin.
BMS has operated its existing DGX SuperPOD for about three years. The company plans to combine it with the Vera Rubin system in a shared computing environment accessible from its research sites worldwide.
The SuperPOD software stack can schedule training, prediction, and development workloads across the infrastructure. BMS said the expanded environment will give more scientists direct access to its computing resources.
Greg Meyers, BMS’s chief digital and technology officer, said computing requirements have increased as the company deploys larger AI models across its research organisation.
Erin Davis, vice president of research business insights and technology at BMS, said the existing infrastructure is operating at capacity. She attributed the demand to large-scale predictions involving large molecules and the development of internal foundation models.
Davis said the new system will not be limited to a small group of computational researchers. BMS plans to make it available across the research organisation without the waiting periods and access limits associated with its current infrastructure.
Applying AI in drug discovery
BMS said AI informs the design of every small-molecule programme and the majority of its large-molecule programmes. The technology is applied to target identification, lead optimisation, large-molecule predictions, and internal model development.
The company said AI-enabled target identification has reduced some manual research work by several weeks. Large-molecule prediction workloads are also contributing to demand for additional graphics processing capacity.
Robert Plenge, BMS’s chief research officer, said the new system will allow scientists to evaluate more potential drug candidates during the early stages of development.
“Maybe before we could do 10 and now we can do dozens,” Plenge said.
Computational screening allows researchers to assess potential compounds before selecting a…
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