Why we started GREENAIBOX

The GPUs were always somewhere else

Before GREENAIBOX, we were the people trying to get a training run started. We had the data, the model and the team. What we never had was the hardware. Capacity existed on paper and disappeared the moment we asked for it. Every project began with a waitlist, and every waitlist ended with a smaller experiment than the one we had planned.

We came to believe that the bottleneck on most AI work was not ideas or talent. It was access to a few thousand GPUs, connected properly, available when the work was ready.

Priced for someone else

When capacity did exist, it was priced for frontier labs and hyperscalers. Quotes assumed a fundraise. Minimum commitments assumed a finance team. A university lab, a startup with a real result or an enterprise team with a budget instead of a war chest could not run the same hardware the frontier runs, even for a week.

We did not think the economics had to work that way. Most of the cost of a GPU-hour is power, cooling and the margin of whoever sits between you and the machine. All three can be engineered down.

Powered by something dirty

The compute we could find was, more often than not, plugged into a grid running on gas and coal, in a building that threw away the heat it produced. Training a model should not carry a carbon footprint that its authors would rather not publish.

Siting next to hydro and wind, and cooling with liquid instead of air, is not a marketing layer. It is the lowest-cost way to build. The cleaner numbers are a consequence of good engineering, not a substitute for it.

So we built what we wanted to rent

GREENAIBOX exists to make world-class AI compute as accessible as electricity: abundant when you need it, priced so that a good idea is enough to afford it, and powered by something you can put in your own sustainability report. That is the whole reason the company exists, and it is the standard we measure ourselves against.