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Bristol Myers Squibb Expands AI Capabilities with New NVIDIA DGX SuperPOD
Bristol Myers Squibb (BMS) has announced the deployment of a second NVIDIA DGX SuperPOD, enhancing its AI capabilities in drug discovery. The new cluster features eight DGX Vera Rubin systems, improving performance and energy efficiency and enabling broader access for scientists across the organization.
Key points
- BMS launches second NVIDIA DGX SuperPOD for AI drug discovery.
- New cluster features eight DGX Vera Rubin systems.
- The setup increases performance per megawatt by up to 10x.
Introduction to the SuperDuperPOD
Bristol Myers Squibb (BMS) has introduced its new NVIDIA DGX SuperPOD, dubbed the ‘SuperDuperPOD’. This advanced AI cluster aims to further enhance the company’s drug discovery capabilities and expand access to AI tools for all scientists within the organization.
Specifications of the New AI Cluster
The latest SuperPOD is constructed with eight NVIDIA DGX Vera Rubin systems, which are noted for being both powerful and energy-efficient. Each system promises to deliver up to 10 times the performance per megawatt compared to previous infrastructures, significantly improving computational resources for researchers.
Accessibility and Impact on Research
BMS aims to democratize access to this technology across its scientific workforce, eliminating limitations on usage. This strategy is intended to accelerate research cycles and broaden the scope of chemical space that can be explored in drug development.
AI's Role in Drug Discovery
The integration of AI in BMS's workflow has already demonstrated practical benefits, such as the AI-enabled target identification process that reduces manual work for scientists. The SuperPOD will facilitate advancements in drug design methodologies, allowing for prioritization based on design predictions.
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Reporting from
Bristol Myers Squibb (BMS) has announced the deployment of a second NVIDIA DGX SuperPOD, enhancing its AI capabilities in drug discovery. The new cluster features eight DGX Vera Rubin systems, improving performance and energy efficiency and enabling broader access for scientists across the organization.