Your Mac, borrowing the cluster

Most teams train in one of two ways: rent a cluster and staff an ML team, or accept the model the vendor shipped. Inference now fits on a laptop more often than training does. Caletta is building a third path for fine-tuning, distillation, and adaptation on operator-owned data.

A laptop will not carry every training run. The proposed bridge keeps data, policy, and resulting weights under operator control while borrowing CUDA capacity for the expensive steps. Local and remote compute are two parts of one run, not opposing deployment choices.

Ownership follows the loop

Ownership does not require every FLOP to run on local hardware. It requires control of the inputs, run policy, evaluation, and resulting weights. Borrowed compute should be replaceable; the capability produced by the run should remain portable.

What exists today

The Go-native MLX runtime performs inference and training on Apple silicon and has a Linux CUDA build path. mlx-go-iroh, built on go-iroh, supplies the public peer-to-peer transport. The self-hostable training coordinator serves a training API on the operator's machine and is publication pending. Membership, quorum, and signed-receipt code is built and tested.

What is not wired yet

The remaining integration is the training data plane across backends. The initial adapter-exchange experiment checks transfer and merge on Apple silicon. Selected Linux CUDA workflows exist in the runtime, but a Mac and a CUDA host have not yet completed a cooperative training run through this stack.

The next acceptance test wires training state onto the peer transport and runs it end to end between Macs and physical CUDA hosts. It must record workload placement, state transfers, synchronization, numerical comparisons, and recovery after a member leaves.