Bristol Myers Squibb supercharges drug discovery with NVIDIA AI
Bristol Myers Squibb Co deployed an NVIDIA Corp DGX SuperPOD with DGX Vera Rubin NVL72 systems to establish the most robust single-owned AI infrastructure in life sciences. The upgrade delivers up to ten times greater performance per megawatt, supporting larger AI workloads across therapeutic areas like oncology and neuroscience. The company is leveraging this infrastructure to train next-generation foundation models and accelerate drug discovery through its "Predict First" approach.

*this image is generated using AI for illustrative purposes only.
Bristol Myers Squibb Co is significantly upgrading its computational capabilities by deploying an NVIDIA Corp DGX SuperPOD equipped with DGX Vera Rubin NVL72 systems. The strategic deployment establishes the most robust and energy-efficient single-owned artificial intelligence infrastructure currently operating within the life sciences sector. The expanded Vera Rubin infrastructure will deliver up to ten times greater performance per megawatt than its predecessor, allowing the company 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 Bristol Myers Squibb first deployed NVIDIA DGX SuperPOD infrastructure. This expansion extends a relationship that matches the growing scope of the company's AI-driven scientific programs across oncology, hematology, cardiovascular, immunology, and neuroscience. The move also builds on the company’s broader AI strategy, including a strategic agreement with Anthropic signed in May to deploy AI across its operations and give more than 30,000 employees access to institutional knowledge.
Strategic Impact on Operations
Bristol Myers Squibb's investment in AI-powered research has already begun to alter how the company discovers and develops medicines. AI agents that automate target identification and validation save scientists weeks of manual work, allowing researchers to focus on hypothesis testing and high-value scientific decisions. Through the company's "Predict First" approach, AI-generated predictions inform experimental design before work begins at the bench. Bristol Myers said AI now supports the design of every small-molecule drug program and most large-molecule development projects.
The enhanced computing capacity will support the development of next-generation foundation models trained on decades of proprietary research data. It also draws on domain-specific capabilities from BioNeMo, NVIDIA's platform for biological AI. The announcement follows another AI-focused partnership unveiled in May with Tempus AI Inc., where the companies agreed to use AI and multimodal real-world data to improve clinical trial design across five initial oncology and neuroscience development programs.
Executive Perspectives
"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."
"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."
| Feature | Description |
|---|---|
| Infrastructure | NVIDIA DGX SuperPOD with DGX Vera Rubin NVL72 systems |
| Efficiency Gain | Up to ten times greater performance per megawatt |
| Key Areas | Oncology, hematology, cardiovascular, immunology, neuroscience |
What is the expected timeline for the new DGX Vera Rubin infrastructure to become fully operational and integrated into existing workflows?
How will the success metrics for the "Predict First" approach evolve as the company transitions to this next-generation computing infrastructure?
Could this level of proprietary AI infrastructure investment trigger a wider trend of in-house supercomputing adoption among other major pharmaceutical companies?






























