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Empire AI is New York State's answer to the compute-access problem, and this is the Cornell route into it. The consortium operates two machines: Alpha+, with 144 NVIDIA H100 GPUs, and Beta, which brings 288 B200 GPUs plus Grace CPU nodes online from December 2025.
Allocations are denominated in Service Units, where one SU buys an hour of H100 time and B200 time costs two SU per hour - a pricing structure worth modelling carefully, since the newer hardware is not automatically the better value for every workload. The economics are the most consequential detail: the Cornell Provost underwrites the cost of allocations through November 2026, after which SUs carry a $0. 50 charge.
Applicants should therefore plan around that transition rather than assume indefinite free access. The current call covers the December 2025 to November 2026 allocation year and closes 17 September at midnight Eastern.
Eligibility is narrow by design - faculty holding PI status at Cornell University, Cornell Tech or Weill Cornell Medicine - so this is not a national programme, but the same Empire AI resource is accessible through parallel calls at the other consortium institutions, and researchers at Columbia, NYU, RPI, SUNY and CUNY campuses should look for their own institution's route.
The application is two-part: a Cornell-specific form plus an Empire AI-hosted form covering project details and compute requirements. Because the consortium is backed by state money and philanthropy rather than a commercial provider, there are no equity, credit-expiry or vendor-lock conditions attached.
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Or search similar grants →According to the current listing, eligibility includes: Restricted to faculty holding Principal Investigator status at Cornell University, Cornell Tech, or Weill Cornell Medicine. Applicants must complete both a Cornell-specific application form and an Empire AI-hosted form addressing project details and compute requirements. The call covers the December 2025 through November 2026 allocation period and closes 17 September at midnight Eastern Time. Awards are Service Unit allocations on the Alpha+ (144 NVIDIA H100 GPUs) and Beta (288 NVIDIA B200 GPUs plus Grace CPU nodes) machines. Allocation costs are covered by the Cornell Provost through November 2026, after which Service Units are charged at $0.50 each. Researchers at other Empire AI consortium institutions in New York State should apply through their own institution's parallel call. Confirm the full requirements in the official notice before applying.
The current listing shows awards are allocations of Service Units (SU) rather than cash. One SU corresponds to one hour of H100 compute on the Alpha+ machine; B200 compute on the Beta machine consumes two SU per hour. The cost of allocations is underwritten by the Cornell Provost through November 2026, after which SUs are priced at $0.50 each. No per-project SU ceiling or dollar value is published in the call. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for Empire AI Consortium GPU Compute Cluster Allocation Call for Proposals (Cornell Route) for AI Research on Alpha+ and Beta are due September 17, 2026. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
Empire AI Consortium GPU Compute Cluster Allocation Call for Proposals (Cornell Route) for AI Research on Alpha+ and Beta is funded by Empire AI Consortium, a partnership of Cornell University and eight other New York State institutions supported by New York State and philanthropic funders. Verify program details on the funder's official page before applying.
This opportunity targets applicants in NY. If your organization operates elsewhere, check the official notice for location requirements.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The CU System Sprint Grant is a small, fast-turnaround internal award supporting faculty who want to develop and implement AI-driven pedagogical strategies within a single course. At up to $20,000 with an explicit allowance for one course buyout, it is designed around the real obstacle facing most faculty experimenting with generative AI in teaching - not equipment or data, but protected time. The single-course framing is a deliberate scoping decision and applicants should respect it: proposals aimed at curriculum-wide or program-level transformation are a poor fit, while a concrete plan to redesign one course around AI tools and measure the effect on student learning outcomes matches what the program is asking for. Eligibility extends to tenured and tenure-track faculty as well as full-time and instructional series faculty across all CU campuses, which is broader than many internal research awards and deliberately includes teaching-focused appointments who are often closest to the pedagogical questions at issue. This is the second of three planned cycles, so faculty who miss the October 16, 2026 deadline should expect a third round. For a grants database this entry is narrower in eligibility than a federal or foundation program - it is open only to CU faculty - but it is a useful example of the institutional micro-grant tier that has grown rapidly as universities respond to generative AI in the classroom, and comparable programs now exist at many university systems. Faculty at other institutions who find this relevant should check whether their own provost or academic affairs office runs an equivalent scheme, since these internal AI teaching grants are frequently under-advertised and undersubscribed relative to external competitions.
The MIT Generative AI Impact Consortium (MGAIC) is an MIT-wide initiative bringing together industry partners and MIT faculty to advance generative AI research with high real-world impact. The consortium awards seed grants to MIT-led research teams across priority areas including: multimodal tactile sensing for robotics, real-time collaborative AI agents (e.g., jam_bots for live human-AI musical improvisation), understanding how LLM agents deviate from human choices and decision-making, foundation models for scientific discovery, generative AI for design and engineering, AI for healthcare and biology, and AI-augmented education. Each consortium funding cycle issues call for proposals from MIT faculty, with industry partner alignment guiding priority areas. Industry members include Analog Devices, Coca-Cola, OpenAI, Tata, Cisco, TWG Global, SK Telecom, McKinsey, Citi, and Verizon. Selected projects benefit from industry collaboration, data sharing, compute access through partner companies, and pathway to commercialization or real-world deployment. The consortium is hosted by MIT Schwarzman College of Computing in partnership with MIT Sloan and benefits from cross-MIT participation including CSAIL, Media Lab, and MIT-IBM Watson AI Lab.
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