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Find similar grantsThis grant provides GPU hours on Nvidia cloud infrastructure for ML research. It requires a faculty PI, but PhD students can be involved.
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Or search similar grants →According to the current listing, eligibility includes: Requires a faculty Principal Investigator (PI); PhD students can be involved. Confirm the full requirements in the official notice before applying.
The current listing shows GPU hours on Nvidia cloud infrastructure (e.g., use of an 8xA100 node for 6 months). Verify award ceilings, matching requirements, and allowable costs in the official notice.
Nvidia Academic Grant is funded by Nvidia. Verify program details on the funder's official page before applying.
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NSF ACCESS (Advanced Cyberinfrastructure Coordination Ecosystem: Services and Support) provides access to dozens of high-performance computing systems including GPU clusters at no cost for academic research and education. The program replaced XSEDE in 2022 and offers tiered allocation levels from small-scale pilot experiments to large-scale research campaigns. Recent hardware upgrades through the NAIRR Pilot added NVIDIA H100 GPUs at multiple sites. Particularly valuable for AI/ML researchers needing GPU time for model training, inference, and large-scale experiments without existing funding.
NVIDIA's Academic Hardware Grant Program donates GPU hardware to qualifying academic researchers, with a specific track focused on AI Safety and Trustworthy AI research (including interpretability, alignment, evaluation, red-teaming, and bias/fairness). Awardees receive workstation- or datacenter-class NVIDIA GPUs delivered to their institutions for use in research projects. Applications are accepted on a rolling basis through the NVIDIA Higher Education and Research portal. Projects must produce a publishable research output or open-source artifact and acknowledge NVIDIA's hardware contribution. The AI Safety track is specifically highlighted by NVIDIA's collaboration with the AI Safety Directory and prioritizes researchers working on interpretability, evaluations, robustness, and trustworthy ML. Strong fit for graduate students, postdocs, and early-career faculty doing AI safety, alignment, or interpretability research at universities without existing GPU access, as well as nonprofit AI safety research organizations.
NVIDIA's Applied Research Accelerator Program provides academic researchers with GPU hardware, cloud compute, and cash support in three tiers: Base ($20K for projects with real-world impact potential), Adoption ($60K for projects with commercial or government adoption plans), and Production ($160K when organizations invest in production conversion). The program supports research with practical applications in GPU-accelerated computing, AI, machine learning, data analytics, and HPC. Applications are reviewed quarterly with four annual cycles: Q1 submissions decided June, Q2 decided September, Q3 decided December, Q4 decided March.
NSF 26-513 makes roughly $100 million available for up to 10 State and Regional AI Infrastructure Hubs at $4M to $12M each over five years. One award per state or multi-state region. One proposal per organization. And NSF is not buying you GPUs — it funds the coordination, the workforce and the faculty training, while the compute has to come from partners you have to already have.
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