Bristol Myers Squibb Becomes First Life Sciences Company to Acquire NVIDIA's Vera Rubin DGX SuperPOD for AI-Driven Drug Discovery
Key Insights
Bristol Myers Squibb (search) is the first life sciences company to purchase an NVIDIA (search) DGX SuperPOD based on the next-generation Vera Rubin architecture, marking a significant escalation in pharma's AI infrastructure arms race.
The new system delivers up to 10 times greater performance per megawatt than predecessor systems, enabling BMS to run larger AI models with greater energy efficiency across all small-molecule and most large-molecule programs.
BMS reports AI tools have already reduced drug development timelines by 20% to 30%, with potential to reach 50% in coming years, and credits AI for the discovery of an experimental sickle cell disease (search) treatment now in early clinical development.
Bristol Myers Squibb (search) (BMS) announced on Monday that it will become the first life sciences company to acquire an NVIDIA (search) DGX SuperPOD based on the chipmaker's next-generation Vera Rubin architecture, a move the drugmaker describes as a "step change" in computational power that will accelerate its artificial intelligence capabilities across drug discovery and development.
The new system, built on NVIDIA (search)'s Vera Rubin NVL72 platform—named after the acclaimed dark matter researcher Vera Cooper Rubin—is specifically designed for complex and autonomous agentic AI workflows and scientific computing. Financial terms of the investment were not disclosed.
"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," said Robert Plenge, chief research officer at Bristol Myers Squibb (search).
Scaling AI Across the Pipeline
The Vera Rubin-based DGX SuperPOD represents a significant upgrade from BMS's existing NVIDIA (search) system, which executives said is approximately two to three generations behind. The new infrastructure will operate with up to 10 times greater performance per megawatt than predecessor systems, enabling the company to "pursue larger and more sophisticated AI workloads without a proportional increase in energy consumption."
Greg Meyers, BMS's chief digital and technology officer, emphasized the energy efficiency gains. "When you host these things, you have to pay an electric bill," Meyers said. "Think of it as 10 times more compute capacity per watt spent ... Electricity is not getting cheaper."
The investment was driven by rapidly growing computing demands as BMS deploys larger AI models across its research organization. The company currently uses AI in all of its small-molecule programs and the majority of its large-molecule programs, employing what it calls a "predict first" approach that precedes any laboratory work.
Measurable Impact on Drug Development
BMS reports that AI tools are already delivering tangible results. According to Plenge, the company has reduced the time required to make medicines for testing in clinical trials by 20% to 30%, with the potential to reach 50% in coming years.
"Maybe before we could do 10 and now we can do dozens," Plenge said, describing how the enhanced computational capabilities will allow the company to cycle through many more potential drug candidates early in the development cycle.
In a striking example of AI's impact, Plenge noted that one experimental sickle cell disease (search) treatment currently in early clinical development would likely not have been discovered without AI-enabled research.
The company's AI agents already automate target identification and validation, saving scientists weeks of manual work and freeing them to focus on hypothesis testing and other scientific decisions. "Drug discovery is a sequence of decisions made under uncertainty, and better decisions come from better evidence, faster," Plenge said.
The Pharma AI Arms Race
BMS's announcement comes just months after Eli Lilly made similar claims regarding its own partnership with NVIDIA (search), stating it did not believe any other company was operating at the scale of its project. The BMS deal underscores an intensifying competition among pharmaceutical companies to build AI infrastructure capable of identifying drug targets faster and improving the probability that experimental drugs succeed in clinical trials.
Meyers said BMS has been building toward this project for nearly three years. "BMS has made a deliberate bet on AI, and we are beginning to see it pay off in our pipeline and operations," he said.
The system will support BMS's "hybrid intelligence" vision, in which AI scientists work alongside researchers developing new medicines. "The goal isn't speed for its own sake; it's raising the probability that each programme we advance is the right one," Plenge added.