TLDR
- Bristol Myers Squibb is deploying a second Nvidia DGX SuperPOD, powered by DGX Vera Rubin NVL72 systems, to expand its AI drug discovery infrastructure.
- The new system delivers up to 10x more performance per megawatt than the previous generation.
- BMS will use Nvidia’s BioNeMo platform and Agent Toolkit for biological and pharmaceutical AI applications.
- The partnership builds on nearly three years of collaboration, with the first SuperPOD already cutting AI-enabled target identification from weeks to days.
- The expansion supports research across oncology, hematology, cardiovascular disease, immunology and neuroscience.
Bristol Myers Squibb (BMY) and Nvidia (NVDA) have expanded their AI partnership, with BMS set to deploy a second Nvidia DGX SuperPOD built on eight DGX Vera Rubin NVL72 systems. BMY stock was up 0.31% and NVDA gained 2.28% on the news.
Bristol-Myers Squibb Company, BMY
The new infrastructure builds on a collaboration that started roughly three years ago, when BMS deployed its first DGX SuperPOD. That system has already delivered results — cutting the time needed for AI-enabled target identification from weeks of manual work to a fraction of that.
📢 𝐉𝐔𝐒𝐓 𝐈𝐍: Bristol Myers Squibb Expands AI Infrastructure With Nvidia DGX Vera Rubin – $BMY $NVDA
👉 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
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➤ Infrastructure includes 𝐃𝐆𝐗 𝐕𝐞𝐫𝐚…
— Hardik Shah (@AIStockSavvy) July 20, 2026
The Vera Rubin architecture is a step up in raw power. Nvidia says it delivers up to 10 times the performance per megawatt compared to the previous generation, meaning BMS can run much heavier AI workloads without a matching rise in energy costs.
The two companies did not disclose the financial terms of the deal.
What the New System Will Do
BMS plans to combine both SuperPODs into a single unified platform accessible across its global research sites. That removes the previous bottleneck where access to high-performance computing was limited to a small group of specialists.
The platform will support everything from training proprietary foundation models to running agentic AI workflows — where AI agents handle tasks like automated target identification and validation with minimal human input.
One of the more interesting approaches here is BMS’s “Predict First” strategy. Instead of synthesizing molecules and testing them in the lab first, scientists use AI predictions to figure out which candidates are worth pursuing before experiments even begin. It’s a way of filtering out weak programs early, directing lab resources where they’re most likely to pay off.
The new system is also expected to help researchers evaluate larger chemical spaces and run more complex molecular predictions.
BioNeMo and Expanding the Pipeline
As part of the expanded deal, BMS gains access to Nvidia’s BioNeMo platform and the BioNeMo Agent Toolkit — software built specifically for biological and pharmaceutical AI applications.
The combination of hardware and software is designed to let researchers run predictions, build agentic workflows and train models using BMS’s own proprietary scientific data.
BMS has also used its existing AI infrastructure to expand its library of CELMoD compounds — engineered molecules that selectively degrade disease-causing proteins. These are currently being investigated across blood cancers and other diseases.
Research areas mapped to the new computing capacity include small molecules, large molecules, clinical applications and digital twins.
Greg Meyers, Chief Digital and Technology Officer at BMS, said the company has “made a deliberate bet on AI” and is starting to see it pay off across its pipeline and operations.
Robert Plenge, Chief Research Officer, framed the goal clearly: “It’s raising the probability that each program we advance is the right one.”
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