Scene 07 · Train
Private Compute
Private, single-tenant compute for generation, training, restoration, and the AI systems we build. Your data, your model, your compute, your perimeter — sized to the job, never on a shared cloud.
Some work cannot go to a cloud API: regulated data, an NDA that means it, a model that is your advantage, anything that should not sit on someone else's servers. The constraint does not change with the size of the job.
Run it on infrastructure we own and operate instead. No queue, no shared tenancy, and the perimeter is yours — scaled to a single run or a standing workload that lives in production.
Questions
- What does "private" actually mean here?
- Your job runs on infrastructure we own and operate, single-tenant, isolated from anyone else. No shared GPU, no third-party cloud, no copy of your data leaving the perimeter.
- Does this only suit small, one-off jobs?
- No. It is sized to the work — a single sensitive run or a standing production workload. The point is sovereignty over your data and models, at whatever scale you operate.
- Can we run our own models and pipelines on it?
- Yes. Bring the work, use the infrastructure. Tell us what you are running and we will scope it.