What is a GPU training control plane?

A GPU training control plane is the management layer that decides where AI training jobs run and keeps them running reliably — handling provisioning, monitoring, checkpointing, and failure recovery — independently of the GPUs that execute the work.

Control plane vs data plane

Borrowing the term from networking and Kubernetes: the data plane is where the actual training runs — the GPU instances executing your script. The control plane is the layer that orchestrates them: choosing capacity, launching and monitoring jobs, syncing checkpoints, and re-launching work when a host fails. Separating the two means you can change where a job runs without changing the job itself.

What a training control plane does

• Provisioning — acquire GPU capacity across your cloud or providers.
• Orchestration — submit, schedule, and track training jobs.
• Checkpointing — persist training state durably as the job runs.
• Monitoring — watch GPU and job health, detect failures.
• Recovery — resume from the last checkpoint on available compute.

How VaultLayer fits

VaultLayer is a BYOC-first training control plane. It runs on top of your own cloud, reserved instances, or GPU contract, and adds the orchestration and recovery layer — so your existing capacity behaves like a managed platform, with external GPU capacity available as overflow.

Frequently asked questions