The Chan Zuckerberg Initiative and NVIDIA have expanded their collaboration to accelerate life science research through virtual cell models. By combining vast biological datasets with GPU-accelerated computing, the partnership aims to process billions of cellular observations and provide researchers with advanced modelling tools to unlock insights into human biology.

Central to this effort is CZI’s Virtual Cells Platform (VCP), an open-source ecosystem giving scientists access to AI models, datasets, and benchmarking tools. The platform allows rapid iteration, standardised evaluation, and reproducible research, enabling faster development of predictive models that reflect the complexity of living cells.

A key challenge in biological research has been scaling data generation and harmonisation. CZI and NVIDIA are addressing this by creating extensive, multi-modal datasets and expanding virtual cell models such as rBio, GREmLN, and TranscriptFormer across multiple biological scales. The combination of CZI’s scientific expertise and NVIDIA’s computing infrastructure accelerates model development while improving accuracy, enabling researchers to test hypotheses on unprecedented scales.

The initiative also integrates NVIDIA Clara Open Models and CodonFM, offering a unified ecosystem for open, community-driven AI research. This approach provides global access to cutting-edge tools, lowering barriers for scientific innovation and creating a standardised environment for collaboration.

While rooted in biology, the collaboration illustrates the transformative power of combining massive data capacity with specialised analytical tools. For industries handling complex operations and logistics, it highlights the value of scaling infrastructure and data processing to drive efficiency, insight, and innovation.

Researchers can now explore rich datasets, AI models, and benchmarks through CZI’s VCP, creating a platform that is as much about accelerating discovery as it is about enabling broad, practical application.

Read the full article to see how this collaboration is redefining AI-driven research and large-scale data application.