NIH grant: PowerEdge XE9680, a NVIDIA H200-powered AI Server (1S10OD040156-01)

source: us-gov-api (us-gov-api:nih-reporter)published 2026-08-01 00:00ingested 2026-08-04 08:36

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NIH grant 1S10OD040156-01: PowerEdge XE9680, a NVIDIA H200-powered AI Server
Institute: {'code': 'OD', 'abbreviation': 'OD', 'name': 'NIH Office of the Director', 'admin_org_id': '12500055', 'admin_funding_url': ''}. PI: Yan Guo. Award: $762427.
Start: 2026-08-01T00:00:00.
Project Summary
The Sylvester Comprehensive Cancer Center (SCCC) at the University of Miami is advancing a transformative
initiative to integrate artificial intelligence (AI) into cancer research, clinical trials, and precision medicine. Central
to this effort is the proposed acquisition of an NVIDIA H200-powered AI server, which will address critical
computational limitations currently hindering large-scale data analysis, deep learning model development, and
multi-omics integration. The server will support high-throughput, GPU-accelerated workflows essential for
applications such as spatial transcriptomics, single-cell omics, and AI-driven drug discovery. SCCC’s
Biostatistics and Bioinformatics Shared Resource (BBSR) will lead the deployment and utilization of this
infrastructure, leveraging its expertise in machine learning, statistical modeling, and translational research. The
H200 server will enable real-time analytics, scalable model training, and collaborative data integration across
shared resources, including the Biospecimen, Onco-Genomics, and Clinical Research Services. It will also
enhance the Sylvester Data Portal by embedding AI and large language models to streamline data access,
harmonization, and hypothesis generation. This investment aligns with SCCC’s 2024–2028 strategic plan and
supports its mission as an NCI-designated Comprehensive Cancer Center. By empowering researchers with
cutting-edge computational tools, the project will accelerate biomarker discovery, improve clinical trial precision,
and foster innovation in cancer care, ultimately improving patient outcomes and training the next generation of
AI-enabled cancer scientists.

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