migration

Plan a GPU-generation migration around usable workload capacity, site readiness, compatibility and a tested rollback path.

Three routes with different costs: keep PyTorch through XLA, bet on the native backend, or adopt the JAX reference trainer. Choose from the model's custom code and the timeline, then prove parity before scaling.

Validate the exact model, accelerator generation and serving features. Separate packed storage from kernel execution, derive the real memory fallback, and compare qualified throughput and total migration cost.

Same model, same engine name, different execution model. Trace what moves into compilation, how shape buckets trade compile time for padding, and how per-device memory decides the slice.