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CZI's Accelerating and Scaling Biological Sciences with AI request for applications awards GPU compute rather than money, allocating time on a cluster of 1,024 Nvidia H100 GPUs to academic teams building large-scale AI and machine learning models for biology. The minimum request is 96 GPUs, which sets a floor on the ambition of eligible projects: this is aimed at training substantial models, not fine-tuning.
CZI prioritises work aligned with building virtual cells, meaning multi-scale neural network models that simulate molecular and cellular behaviour, though any biomedical research supporting CZI's disease-prevention mission qualifies. Projects must complete within a maximum one-year timeline and demonstrate they can genuinely scale to 96 or more GPUs, a technical bar that eliminates many otherwise strong proposals.
The open-science conditions are real obligations rather than boilerplate: grantees must release code under MIT, BSD or Apache licences, make datasets publicly available, follow preprint publication protocols, and file technical reports.
Applications were accepted on a rolling basis with review dates on the fifteenth of January, February, March and April, and CZI reserved the right to close the portal early once cluster capacity was fully allocated - which is the practical constraint, since the resource is finite and allocations are not renewed automatically. Teams should check the CZI RFA page for the current cycle.
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Or search similar grants →According to the current listing, eligibility includes: Eligible applicants are United States-based nonprofit organizations including colleges, universities, hospitals, research laboratories and government agencies. The principal investigator and any co-principal investigators must hold faculty or equivalent independent investigator status at a domestic United States nonprofit institution. For-profit organizations, Meta employees and subsidiary entities, and non-United States institutions are explicitly ineligible. A maximum of three co-principal investigators plus one coordinating principal investigator is permitted, and organizations may submit multiple applications provided the scope does not overlap. Projects must demonstrate the ability to scale to a minimum of 96 GPUs and must complete within a maximum one-year timeline. The award is an in-kind GPU compute allocation only; no cash funds are provided. Grantees must comply with open-source code sharing under MIT, BSD or Apache licences, make datasets publicly available, follow preprint publication protocols, submit final technical reporting, meet ethical research standards and attend progress meetings. Applications were accepted on a rolling basis with review dates on the fifteenth of January, February, March and April at 1 p.m. Pacific, and CZI reserved the right to close the portal early once cluster capacity was allocated. Confirm the current cycle status, review dates and eligibility on the CZI RFA page before applying. Confirm the full requirements in the official notice before applying.
The current listing shows this award is entirely in-kind: recipients receive an allocation of GPU compute on CZI's cluster of 1,024 Nvidia H100 GPUs, with a minimum allocation request of 96 GPUs. No cash funds or financial contributions are provided, so amount_min and amount_max are recorded as 0 because there is no monetary award. The economic value is nonetheless substantial - a 96-H100 allocation held for up to a year would cost well into seven figures at commercial cloud rates - and for teams whose bottleneck is compute rather than salary this is often more useful than an equivalently sized cash grant. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Chan Zuckerberg Initiative Accelerating and Scaling Biological Sciences with AI GPU Compute Request for Applications is funded by Chan Zuckerberg Initiative (CZI). Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.