The National Institutes of Health's All of Us Research Program illustrates how much computing power modern medical research already demands. In its June 2026 data release, the program made genomic and health data from more than 747,000 participants available to registered researchers, bringing total enrollment past 883,000. The dataset now includes more than 535,000 whole genome sequences, 1.3 billion genetic variants, and nearly 482,000 linked electronic health records, all housed on a secure Researcher Workbench built on Google Cloud Platform. “There’s a paradox at the heart of precision medicine,” NIH Director Jay Bhattacharya said in announcing the release. “To tailor treatments to individuals, you actually need very large populations to uncover the patterns that connect genetics, lifestyle, and the environment to health outcomes.”
That kind of population-scale genomic analysis runs on infrastructure most people never see. Biowulf, the NIH’s in-house Linux computing cluster, is described by the agency’s Center for Information Technology as the world’s most powerful supercomputer dedicated solely to biomedical research. It has grown to more than 100,000 processor cores and 60 petabytes of storage, is used by roughly 75% of NIH principal investigators, and in fiscal year 2024 alone consumed more than 1 billion core-hours and 6 million GPU-hours. The system helped the Telomere-to-Telomere Consortium publish the first complete, gapless human genome sequence in 2022, and NIH says COVID-19 research on Biowulf consumed more than 87 million CPU hours and produced more than 50 peer-reviewed publications.
That demand is emerging just as the broader market for AI compute tightens. Apollo Global Management’s wealth insights team described on-demand GPU capacity as “effectively sold out” in a June 2026 analysis, noting that rental rates for Nvidia H100 GPUs rose from roughly $1.85 an hour in late 2025 to about $2.40 an hour by March 2026, while spot prices for high-bandwidth memory chips climbed roughly eightfold since early 2025. Apollo named healthcare specifically as one of the sectors “only beginning to deploy AI at scale” just as that squeeze sets in, alongside legal services and financial analysis. Data Center Knowledge reported the binding constraint has shifted from the power shortages that dominated 2024 and 2025, when Microsoft CEO Satya Nadella said the company had “a bunch of chips sitting in inventory that I can’t plug in,” to semiconductor manufacturing capacity itself. “Silicon is the binding short-term constraint. Power is the binding long-term constraint,” HyperFrame Research’s Stephen Sopko told the outlet.
Drug makers with the capital to do so are responding by building and owning compute capacity rather than competing for it on the open market. Recursion Pharmaceuticals brought online BioHive-2, a supercomputer built with Nvidia using 504 H100 GPUs delivering 2 exaflops of AI performance, which debuted at No. 35 on the TOP500 list of the world’s most powerful supercomputers. Recursion’s chief technology officer said the company can now get “80% of the value with 40% of the wet lab work” that traditional drug discovery required. Roche has gone further, announcing an AI factory spanning more than 3,500 GPUs across facilities in Europe and the United States, built on Nvidia’s newest Blackwell chips and expected to be fully operational by early 2027. “Everybody wants to get their hands on Nvidia chips,” biopharma AI consultant Christian Hein told SWI swissinfo.ch.
Hospitals, by contrast, mostly cannot build their own supercomputers and instead depend on outside vendors and cloud infrastructure for the same AI capacity pharma companies are racing to own. In 2026, the U.S. Department of Veterans Affairs began scaling its ambient AI scribe tool from a 10-site pilot to all of its more than 130 medical centers nationwide. Mount Sinai Health System integrated OpenEvidence’s AI clinical decision support directly into its Epic electronic health record system across all seven of its hospitals. HonorHealth rolled out Abridge’s ambient documentation platform to roughly 3,000 physicians and advanced practice providers, and the University of Texas System’s earlier pilot with Qualified Health generated more than $15 million in run-rate financial impact within six months before expanding across all eight of its health institutions. Each of these systems now depends on rented, high-capacity cloud infrastructure to keep those tools running, in a market where that same infrastructure is getting harder and more expensive to secure.
That dependence is starting to show up in how healthcare organizations plan their own facilities, too. Commercial real estate firm Colliers has noted that hospitals and health systems increasingly compete with data center developers for the same scarce inputs, reliable electricity, available land, and network connectivity, while their own IT infrastructure has to support higher computing density and GPU-ready systems that older hospital data centers were never built for. For an industry historically organized around clinical staffing and bed capacity, the amount of processing power behind a diagnosis, a drug candidate, or a documentation tool is quickly becoming a resource healthcare organizations can no longer take for granted.
- NIH — “NIH's All of Us Research Program is now the largest integrated genomics and health database in the world”
- NIH Center for Information Technology — “Biowulf Slays Research Challenges as the World's Largest Biomedical Supercomputer”
- NVIDIA Blog — “Drug Discovery, STAT! NVIDIA, Recursion Speed Pharma R&D With AI Supercomputer”
- SWI swissinfo.ch — “Explainer: Why Roche's supercomputer is a big deal”
- Becker's Hospital Review — “15 health systems that have signed enterprise AI deals in 2026”
- Colliers Knowledge Leader — “The Data Center Boom Has Implications for Healthcare Real Estate”
- All of Us Research Program Support — “Getting Started and What to Know About Costs”
- Apollo — “The Growing Compute Shortage”
- Data Center Knowledge — “After the Power Crunch, AI Infrastructure Hits a GPU Wall”