Bristol Myers Squibb Just Gave Every One of Its Scientists Access to the Biggest AI Computer in Drug Research

Key points

• Bristol Myers Squibb (BMS) is deploying its second NVIDIA DGX SuperPOD, built on eight Vera Rubin NVL72 rack-scale systems, making it the most powerful AI cluster in life sciences.
• The new system delivers up to 10 times the performance per unit of electricity compared to the hardware it replaces.
• BMS will give every scientist at the company access to the system, with no waiting lists and no usage caps.
• BMS has run an earlier AI supercomputer for roughly three years, using it to cut weeks off drug-target research and expand its library of cancer-fighting molecules.
• The combined setup will let researchers type requests in plain English rather than needing deep computing expertise.

Erin Davis has a new nickname for Bristol Myers Squibb's computing setup: the "SuperDuperPOD." It sounds playful. The science behind it is not.

BMS, one of the world's largest pharmaceutical companies, already runs one of the biggest AI computing clusters in the life sciences industry. This week, reported by NVIDIA, the company announced it is adding a second, even more powerful cluster on top of it. The new system is built on eight DGX Vera Rubin NVL72 units, rack-sized computers packed with Rubin GPUs (the specialised chips that do the heavy number-crunching AI needs) and Vera CPUs (general-purpose processors that direct the overall workload).

The headline number: the new cluster delivers up to ten times the computing output per megawatt of electricity compared to the system it replaces. That matters in drug discovery because some AI predictions are genuinely enormous, running across millions of possible molecules at once.

But the part Davis is most excited about is not the raw power. It is access.

"Instead of equipping a small group of researchers with access to the supercomputer, we're opening it up to literally every scientist," says Davis, vice president of research business insights and technology at BMS. "No one has to wait, and no one is told they have a limit."

Previously, site-specific restrictions left over from old company acquisitions meant many researchers could not easily reach the system at all. The new setup connects BMS labs globally into a single platform. A team in Lawrenceville, New Jersey, and a team in San Diego, California, will draw from the same pool of data and models. Researchers will be able to kick off complex predictions by typing a question in plain English, rather than submitting a formal computing request.

What does this mean for patients waiting on new medicines?

Faster access to computing should mean shorter gaps between scientific ideas and testable drug candidates. BMS has spent the past three years using its first AI cluster to cut weeks of manual target-identification work, and to expand a library of molecules designed to break down cancer-causing proteins. Payal Sheth, senior vice president of therapeutic discovery sciences at BMS, describes a "Predict First" approach: scientists run AI predictions on a molecule's likely behaviour before committing lab time to building it. That weeds out dead ends early and keeps expensive experiments focused on the most promising candidates.

The company is also building what it calls agentic workflows, where AI agents (software that can carry out multi-step tasks on its own, including searching data and running predictions across different departments) connect learning from separate research programmes. Every experiment, Sheth says, now feeds a shared intelligence system that compounds over time. That did not exist at the start of her career.

Davis's motivation is personal as well as professional. Her father died of Alzheimer's disease five years ago. BMS has a significant investment in brain health research, an area she describes as "notoriously hard." The compute, she says, is not the goal. It is the means to get somewhere faster.

The combined system launches with a detailed workload plan already mapped across small-molecule design, large-molecule design, clinical applications and digital twins (computer simulations of biological processes). "We didn't just buy this to have the biggest compute," Davis says. "Welcome to Limitless Compute."