Bristol Myers Squibb Expands AI Research with NVIDIA Vera Rubin SuperPOD
Bristol Myers Squibb said in a press release that it will deploy the latest NVIDIA DGX Vera Rubin NVL72 systems to expand its computing infrastructure for drug discovery and development. The installation will form what the company describes as the most powerful and energy efficient NVIDIA setup in the life sciences sector, offering up to ten times higher performance per megawatt than previous systems.
The new cluster will support BMS’s proprietary AI models and agentic workflows under a unified computational backbone. These systems will enable researchers to manage more complex workloads without a proportional rise in energy use. The initiative extends a collaboration that began nearly three years ago when BMS first adopted NVIDIA’s SuperPOD technology.
In a company blog post, NVIDIA said the expanded deployment consists of eight DGX Vera Rubin NVL72 systems that will provide access to a unified AI platform and the BioNeMo Agent Toolkit for biological research. The infrastructure will be shared across BMS sites globally, facilitating predictions, model training, and automation in screening and molecular design.
BMS executives said the new capabilities will allow scientists to accelerate discovery processes and prioritize the synthesis of molecules more effectively, with AI informing both small and large molecule programs across disease areas including oncology, immunology, and neuroscience.
We hope you enjoyed this article.
Consider subscribing to one of our newsletters like AI Funding Brief, Life AI Weekly or Daily AI Brief.
Also, consider following us on social media:
More from: Funding
More from: Life Sciences
Subscribe to AI Funding Brief
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read more