Model Lifecycle Capacity Handoff Between Training and Serving

The model lifecycle capacity handoff is the transfer of a newly trained or updated model from training infrastructure to serving infrastructure — and without planning, it is where new models sit idle waiting for capacity that was never reserved, or worse, disrupt serving by consuming capacity that production inference needs. For the training-vs-inference capacity planning split, see capacity planning for training vs inference.

Why the Handoff Breaks Without Planning

Training and serving share GPUs in many clusters, but they compete. A newly trained model ready for deployment needs serving GPU capacity — memory for weights and KV cache, concurrency headroom, latency budget — that may already be occupied by existing serving workloads or by the next training job. Without a planned handoff, the model waits for capacity to open, or the team evicts something else to make room, disrupting the cluster's operation. The handoff must answer three questions: which GPUs will serve the new model (reserved in advance or allocated at handoff time), how the model artifact transitions from training to serving storage (validated and ready before the handoff), and how the existing serving deployment transitions (canary, shadow, or direct cutover).

Planning the Handoff

Reserve serving capacity before the training run completes — a fraction of the cluster's inference partition or a pooled capacity reservation that the scheduler honors when the handoff triggers. This prevents the "model is ready, GPUs are not" scramble. Transition the artifact: the training pipeline produces the validated, packaged model artifact and places it in a location the serving infrastructure can reach — verified as loadable, correct, and meeting quality bars — before the handoff. Execute the rollout: canary, shadow, or direct cutover depending on the risk tolerance, with rollback ready. For the deployment process, see how AI model deployment works. For the orchestration that coordinates this, see how AI orchestration works.

FAQ

How do I plan capacity for model handoff from training to serving?

Reserve serving capacity before training completes, transition the validated artifact to a location the serving infrastructure can reach, and execute rollout with rollback ready. Plan the handoff as a deliberate step in the model lifecycle, not an afterthought. For the capacity planning split, see training vs inference planning.

What happens when training and serving compete for the same GPUs at handoff?

Without planning, the handoff scrambles — the new model waits for capacity, or someone evicts existing workloads. With planning, serving capacity is reserved, the artifact transitions pre-validated, and the rollout proceeds without disruption. The difference is whether the handoff is planned into the lifecycle. See above.

Summary

Model lifecycle capacity handoff requires reserve-planning, artifact transition, and controlled rollout — planned before the training run completes. Without planning, new models wait idle or disrupt production. For the full lifecycle and orchestration framework, see capacity planning for inference and how AI orchestration works.