Supercomputers/Systems/Mayo Clinic DGX SuperPOD
USA · operational · ai training
Mayo Clinic DGX SuperPOD
Operated by Mayo Clinic at Mayo Clinic Rochester Campus .
Phase 1 seed dataset, compiled by hand. These rows were built from public operator, laboratory and vendor sources. A mechanical second-reader pass has since fetched every cited source: 1092 of 1153 systems have a readable citation that names them, and 40 are genuinely weakly sourced. Every claim carries its source and a confidence tier. Treat anything below verified as a lead, not a citation.
Measured performance
- Rmax
- -
- Rpeak
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- Rmax ÷ Rpeak
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- Cores
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- Power
- -
- Per watt
- - GF/W
Figures are as last publicly reported for the configuration described below, not a live measurement. Where a system was upgraded in place, the post-upgrade configuration is shown and the earlier one appears in the timeline.
What this machine is made of
One row per supplier relationship. “Supplier at build” is the company that shipped the part at the time; where that company has since been acquired, the parent it rolls up to today is shown beside it. That distinction is what makes ticker-level aggregation possible across a thirty-year dataset.
| Role | Part | Supplier at build | Quantity | Confidence | Source |
|---|---|---|---|---|---|
| Integrator | - NVIDIA DGX SuperPOD with NVIDIA DGX B200 systems, Blackwell architecture, deployed as a tightly-integrated AI compute cluster | NVIDIA | - | Reported | kttc.comhitconsultant.net |
| Accelerator | NVIDIA B200 SXM Accelerators CUDA (Blackwell) · TSMC 4NP · 1,000 W NVIDIA DGX SuperPOD with NVIDIA DGX B200 (Blackwell) systems; "the supercomputer has 128 GPUs" per Mayo Clinic Digital Pathology CEO Jim Rogers | NVIDIA | 128 reported | Reported | kttc.com |
| Storage | - DDN storage enclosures reported as part of the surrounding infrastructure (alongside Dell servers and Panduit racks); no quantities given | DDN | - | Reported | rcrwireless.com |
The read
Mayo Clinic's first large-scale healthcare deployment of NVIDIA's latest AI silicon: an NVIDIA DGX SuperPOD built from NVIDIA DGX B200 (Blackwell) systems, deployed and announced in late July 2025 to accelerate digital pathology (including the Atlas foundation model built with Aignostics on 1.2 million histopathology whole-slide images), drug discovery and precision-medicine foundation-model work. Local Rochester, MN television coverage (KTTC, citing Mayo Clinic Digital Pathology CEO Jim Rogers) states the system has 128 GPUs and refers to Rochester as the 'Med City' location; the healthcare-IT trade outlet HitConsultant independently confirms the DGX SuperPOD / DGX B200 naming but not the exact GPU count, so this row stays at reported confidence rather than verified. A third outlet, RCR Wireless, additionally reports Dell servers, DDN storage enclosures and Panduit racks around the deployment but does not tie those to specific quantities. No FLOPS figure, core count or power draw was stated in any source fetched, so those fields are left blank rather than estimated.
Site & facility
Change history
- 2026-10-01 Mayo Clinic DGX SuperPOD added, an NVIDIA DGX SuperPOD built from DGX B200 (Blackwell) systems that Mayo Clinic deployed in July 2025 for digital pathology and precision-medicine AI
Source check
We fetched this system's own citations and recorded whether each page actually mentions it. This is a corroboration signal, not a fact check, and it is published so you can see how well the sourcing holds up rather than take it on trust.
| Cited source | Result | Found on the page |
|---|---|---|
| hitconsultant.net | names system and part | NVIDIA B200 SXM |
| kttc.com | names system only | · counts: 128 |
Checked 2026-10-09 by pnpm verify. Re-run it and the table changes with the web.
Further reading
Inside a node
Why accelerators exist, what the memory hierarchy costs, and how the CPU and accelerator merged onto one package.
Operating a supercomputer
Acceptance testing, why "installed" and "in production" are six months apart, and the machine lifecycle.
Storage and I/O
Parallel file systems, why checkpointing dominates the write load, and the metadata failure mode nobody expects.