The partnership aims to enable Verily’s health, life science, payer, and government partners to build and implement AI solutions more efficiently across healthcare.
Verily, a precision health AI company, has announced a strategic partnership with NVIDIA to integrate NVIDIA’s full AI technology stack into its Pre platform.
The collaboration aims to help Verily’s health system, life sciences, payer, and government partners build and deploy AI solutions more efficiently across healthcare.
Through this integration, researchers gain access to NVIDIA’s advanced AI tools and hardware within Verily’s trusted research environment (TRE), Workbench. Verily has incorporated GPU-accelerated libraries and frameworks such as NVIDIA NeMo, Parabricks, and CUDA-X Data Science, along with GPUs like NVIDIA Blackwell and Hopper.
This integration has already delivered major performance gains—cutting genomic data processing times from hours to minutes using Parabricks and B200 GPUs compared to CPU-only systems.
“With NVIDIA’s cutting-edge AI capabilities now part of our Pre platform, we’re equipping researchers with powerful tools to accelerate AI model development and omics analysis,” said Stephen Gillett, CEO of Verily.
A key part of the partnership focuses on enhancing analyses within the National Institutes of Health’s (NIH) All of Us Researcher Workbench. Supporting nearly 20,000 researchers globally, the Workbench hosts one of the world’s largest genomics datasets and will now run on Verily’s Pre platform through an extended collaboration with Vanderbilt University Medical Center.
Using this data, Verily researchers created the first multimodal foundation model combining EHR and genomic data through polygenic risk scores to advance disease prediction and proactive health management. With NVIDIA NeMo Automodel and H100 GPUs, model training speeds improved tenfold over previous methods.
Verily also plans to expand NVIDIA GPU-accelerated tools across its broader Pre solutions. The Refinery platform will continue curating and structuring multimodal data using a FHIR-native model, while Exchange will provide a secure space for sharing AI-ready datasets, models, and agents to drive precision research and care.
