Bristol Myers Squibb is ramping up its artificial intelligence ambitions with a major expansion of its computing infrastructure, partnering with NVIDIA to build what the company says will be the most powerful and energy-efficient single-owned NVIDIA AI system in the life sciences industry.

 

The biopharmaceutical giant will deploy NVIDIA DGX Vera Rubin NVL72 systems as part of an expanded NVIDIA DGX SuperPOD, giving BMS researchers unprecedented computing power to scale proprietary AI models, shorten drug discovery timelines and advance its vision of “hybrid intelligence” — where AI scientists work alongside human researchers to develop first- and best-in-class medicines.

 

The new Vera Rubin infrastructure is expected to deliver up to ten times greater performance per megawatt compared with NVIDIA’s previous-generation architecture, allowing BMS to run larger and more complex AI workloads while improving energy efficiency.

 

The investment builds on nearly three years of collaboration between BMS and NVIDIA, following BMS’s initial deployment of NVIDIA DGX SuperPOD systems to support research and development programs. The expanded platform will support AI-driven scientific initiatives across oncology, hematology, cardiovascular disease, immunology and neuroscience.

 

“BMS has made a deliberate bet on AI, and we are beginning to see it pay off in our pipeline and operations,” said Greg Meyers, Chief Digital and Technology Officer at Bristol Myers Squibb. 

 

“We're committed to translating AI into real outcomes for patients which requires infrastructure built to match that ambition. Expanding our compute capabilities with NVIDIA gives our researchers and teams across the business the scale they need to keep BMS at the leading edge of what AI can do for drug discovery & development.”

 

BMS said its AI-powered research efforts are already reshaping how medicines are discovered and developed. AI agents are helping scientists automate target identification and validation, reducing weeks of manual analysis and allowing researchers to focus on higher-value scientific decisions.

 

The company’s “Predict First” approach uses AI-generated predictions to guide experimental design before laboratory work begins. BMS said AI now informs the design of every small molecule program and the majority of its large molecule programs.

 

The company is building an integrated AI-powered learning system that connects early target discovery through clinical proof of concept, helping researchers make more informed decisions throughout the development process.

 

"Drug discovery is a sequence of decisions made under uncertainty, and better decisions come from better evidence, faster,” said Robert Plenge, Executive Vice President and Chief Research Officer at Bristol Myers Squibb. 

 

“This infrastructure lets us learn from every experiment and every clinical readout to sharpen the next hypothesis, allowing BMS scientists to spend less time on manual work and more time on the questions that require human judgment. The goal isn't speed for its own sake; it's raising the probability that each program we advance is the right one.”

 

The expanded NVIDIA collaboration supports BMS’s goal of creating a new model of scientific discovery in which AI systems handle complex, data-intensive tasks while researchers focus on interpretation, strategy and critical scientific judgment.

 

The Vera Rubin cluster will serve as the foundation for next-generation AI models trained on BMS’s decades of proprietary scientific data. The system will also support specialized biological AI capabilities through NVIDIA’s BioNeMo platform and enable AI-driven workflows designed to evaluate scientific hypotheses at a scale previously impossible.

 

"BMS has built decades of extraordinary scientific knowledge across some of the most complex areas of human disease,” said Rory Kelleher, Senior Director of Business Development, Healthcare and Life Sciences at NVIDIA. 

 

“With NVIDIA Vera Rubin and BioNeMo Agent Toolkit, BMS has the ability to transform that enterprise scientific data into proprietary intelligence and give agents domain-specific tooling that help scientists explore biology, design molecules and evaluate hypotheses at unprecedented scale — reinventing how medicines are discovered and developed.”