Bristol Myers Squibb to Build the Most Powerful AI Factory in Life Sciences with NVIDIA

Bristol Myers Squibb, a global biopharmaceutical leader, announced it will expand its compute infrastructure to deploy an NVIDIA DGX SuperPOD with DGX Vera Rubin NVL72 systems, giving BMS the most powerful and energy-efficient single-owned NVIDIA infrastructure in life sciences.

The expanded Vera Rubin infrastructure will give BMS’s scientific and computational teams access to a step-change in both computational power and efficiency, with the Vera Rubin architecture delivering up to ten times greater performance per megawatt than its predecessor, enabling BMS to pursue larger and more sophisticated AI workloads without a proportional increase in energy consumption.

The investment builds on nearly three years of collaboration that began when BMS first deployed NVIDIA DGX SuperPOD infrastructure to support its Research and Development activities, and marks the next step in expanding that foundation to match the growing scope and sophistication of BMS's AI-driven scientific programs across oncology, hematology, cardiovascular, 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’s investment in AI-powered research has already begun to change how the company discovers and develops medicines, and these early results span multiple areas of drug discovery. For example, AI agents that automate target identification and validation save BMS scientists weeks of manual work, freeing them to focus on hypothesis testing and the highest-value scientific decisions. From there, through the company’s “Predict First” approach, a methodology where AI-generated predictions inform experimental design before work begins at the bench, AI informs the design of every small molecule program and the majority of the company’s large molecule programs. These are just two examples that reflect BMS’s broader approach: building an integrated, AI-powered learning system that spans target identification through clinical proof of concept, helping scientists make higher-confidence decisions at every stage.

“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.”