fine tuning

Size adapter and full-parameter jobs before comparing RTX PRO 6000 and H100. Test memory headroom, MIG packing, software support and real communication costs.

Separate packed base weights, quantization metadata and trainable adapter state. Count the real projection shapes, then leave room for activations and temporary work before calling a one-card configuration feasible.

Version teacher outputs, separate learning from evaluation, and catch a release whose domain score improves while a protected capability regresses.

Group compatible adapters by pinned base, derive their size from target matrices and ranks, and benchmark mixed batches. Catalogue size, CPU residency, GPU slots and active adapters are separate limits; base sharing does not make fifty variants operationally free.

Separate frozen weights from trainable state, count rank-16 adapters from matrix shapes, and check peak memory before choosing a fine-tuning layout.

Balance durable checkpoint cost against replay and restart loss, with explicit assumptions and a warning sign when the approximation breaks down.

Choose the tenant boundary before planning base-model reuse. Account for frozen weights, adapter state and activations separately, then schedule by compatibility, fairness and the real cost of restarting training.

Derive adapter bytes from the target matrices, share compatible base weights, and keep each request's cache identity separate. A worked pool design avoids applying the mixed-adapter cost twice.

Follow the matrix shapes through a low-rank update. Verify merged inference numerically and show why multiplying sums of adapter factors gives the wrong weights.