Borrowed Iron

When a partner like NVIDIA believes in what you are building and hands you a cluster of eight H100s for a couple of months, you do one thing with it: make the most of every hour you have.

That is what this series is. SocialEyes is building AI that reads systemic health from retinal images, NVIDIA backed the mission with the compute to push it hard, and we are writing the whole build up in public as it happens. The plan is to smoke the cluster, get the absolute most out of it while we have it, and do right by the people who bet on us by sharing exactly how.

Each part is a self-contained engineering story, scrubbed of anything proprietary and written so you can apply it to your own borrowed or rented GPUs. The parts walk through the real work: standing up the box and giving a team access to it, staging terabytes of public retinal data, locking an ML recipe from the literature before spending a GPU-hour, training our own retinal foundation model, serving a reasoning model locally with speculative decoding, and the through-line that made all of it cheaper, how months of small-box homelab experiments de-risked the big borrowed one.

New parts land as the work happens. Follow the lab below to get each one when it ships.