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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →NVIDIA and the U.S. National Science Foundation (NSF) announced $152 million in support for Ai2’s Open Multimodal AI Infrastructure to Accelerate Science (OMAI) on August 14, 2025. The project aims to build openly available multimodal AI models and infrastructure for scientific research. Scientists are intended to be able to use its models and software at low or no cost, but the announcement did not provide a release date or a process for getting access.
What did NVIDIA and NSF announce?
OMAI is a national research-infrastructure effort led by the Allen Institute for AI (Ai2). Its goal is to develop open AI models and supporting tools for scientific discovery, while also advancing AI research. The partnership combines NSF funding with NVIDIA compute and software support.
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This is a research initiative, not the launch of a consumer AI product. NVIDIA identified research teams at the University of Washington, University of Hawaii at Hilo, University of New Hampshire and University of New Mexico as beneficiaries.
How much is the partnership worth?
The announced public-private support totals $152 million. NSF’s 2025 year-in-review lists its $75 million contribution; NSF’s FY2025 Agency Financial Report records NVIDIA’s $77 million contribution.
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- [Double-Flow-Through Design] The RTX PRO 6000 Blackwell features a double-flow-through cooling design, optimizing efficiency and airflow to sustain peak performance under 600W power loads. | [5th Gen Tensor Cores] Deliver up to 3X the performance of the previous generation and support for FP4 precision for faster AI model processing times with reduced memory usage, enabling local fine-tuning of LLMs and generative AI | [4th Gen Ray Tracing Cores] Double the ray-triangle intersection rate of the previous generation to create photoreal, physically accurate scenes and immersive 3D designs with RTX Mega Geometry, which enables up to 100X more ray-traced triangles.
- [PCIe Gen 5] Support for PCIe Gen 5 provides double the bandwidth of PCIe Gen 4, improving data-transfer speeds from CPU memory and unlocking faster performance for data-intensive tasks like AI, data science, and 3D modeling. | [GDDR7 Memory] With 96 GB of GPU memory and 1.8 TB ps bandwidth, it can tackle massive 3D and AI projects, fine-tune AI models locally, explore large-scale VR environments, and drive larger multi-app workflows.
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| Contributor | Support | What the announcement identifies |
|---|---|---|
| National Science Foundation | $75 million | NSF funding for OMAI, listed in its 2025 year-in-review. |
| NVIDIA | $77 million | Contribution recorded in NSF’s FY2025 Agency Financial Report; NVIDIA also supplies computing systems and software. |
| Total | $152 million | Combined public-private support announced for the project. |
What is NVIDIA providing?
NVIDIA says it is supplying HGX B300 systems built with Blackwell Ultra GPUs, along with NVIDIA AI Enterprise software for model training and inference. Cirrascale Cloud Services is the managed-services provider for the new hardware infrastructure.
What does “open” mean for OMAI?
The stated aim is to make more than model outputs available to researchers. The partnership says model data, tools for interrogating and refining datasets, documentation and training for early-career researchers will be openly available. Ai2 describes the software and models as intended to be offered at low or zero cost to researchers.
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Access to training data could help researchers investigate how model behavior relates to particular training examples. Ai2 senior director Noah Smith said, “With the model training data in hand, you have the opportunity to trace back to particular training instances similar to a response, and also more systematically study how emerging behaviors relate to the training data.” That points to potential benefits for inspection and reproducibility; it does not by itself establish the specific license terms or access conditions for each future model.
Can scientists use the models now?
The commitment is that researchers will be able to use OMAI’s models and software at low or zero cost. However, the August 2025 announcement did not name a first model, give a release date, publish benchmark scores, specify licensing details beyond the broad open-access commitment, or describe a formal process for researchers to request access. So the announcement establishes the intended direction, not that a particular model is already available to download or use.
Rank #3
- NVIDIA Ampere Architecture-based CUDA Cores - Double-speed processing for single-precision floating point (FP32) operations and improved power efficiency provide significant performance improvements for graphics and simulation workflows, such as complex 3D computer-aided design (CAD) and computer-aided engineering (CAE), on the desktop.
- Second-Generation RT Cores - With up to 2X the throughput over the previous generation and the ability to concurrently run ray tracing with either shading or denoising capabilities, second-generation RT Cores deliver massive speedups for workloads like photorealistic rendering of movie content, architectural design evaluations, and virtual prototyping of product designs. This technology also speeds up the rendering of ray-traced motion blur for faster results with greater visual accuracy.
- Third-Generation Tensor Cores - New Tensor Float 32 (TF32) precision provides up to 5X the training throughput over the previous generation to accelerate AI and data science model training without requiring any code changes. Hardware support for structural sparsity doubles the throughput for inferencing. Tensor Cores also bring AI to graphics with capabilities like DLSS, AI denoising, and enhanced editing for select applications.
- Third-Generation NVIDIA NVLink - Increased GPU-to-GPU interconnect bandwidth provides a single scalable memory to accelerate graphics and compute workloads and tackle larger datasets.
- 48 Gigabytes (GB) of GPU Memory - Ultra-fast GDDR6 memory, scalable up to 96 GB with NVLink, gives data scientists, engineers, and creative professionals the large memory necessary to work with massive datasets and workloads like data science and simulation.
Why are the partners investing in it?
Ai2 and its partners frame OMAI as a way to give scientists access to AI capabilities and infrastructure while supporting research that can be inspected, reproduced and adapted to scientific work. NVIDIA also presents the effort as aligned with the White House AI Action Plan and as an investment in U.S. leadership in science and engineering. NSF places OMAI among its broader AI-leadership investments; its separate NAIRR pilot connected more than 600 research teams in 2025, a figure that should not be read as OMAI participation.
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