What changed
AWS launched SageMaker Inference for Nova models customized through continued pre-training, supervised fine-tuning or reinforcement fine-tuning. Teams can choose G5 or G6 instances, configure autoscaling, context length and concurrency, and tune deployment economics instead of using a fixed managed endpoint.
Why it matters
Model customization only becomes operationally useful when teams can control serving hardware, capacity and latency separately from training.
What to do
Load-test a customized checkpoint against the base model with the same traffic trace; compare quality, tokens per second, cold starts, utilization and total cost.
Caveat
AWS's cost and utilization benefits are architecture- and workload-dependent and do not remove the need to evaluate the customized model for regressions.