Every byte moved has a cost. As model checkpoints grow to hundreds of gigabytes or even a terabyte, that cost adds up quickly. To make things even worse, moving these model weights around the cluster is extremely common. For instance, a cold start may pull weights from remote storage into GPU memory; autoscaling and rolling updates must populate each new replica; and RL post-training continuously moves updated weights from trainers to roll out workers. These may look like different workflows, but they impose the same recurring tax: time spent moving weights before useful work can begin.

ModelExpress: Accelerating the model weight lifecycle

NVIDIA ModelExpress (MX) is built around a simple idea: Before loading a model, first ask where a compatible copy of its weights already lives.…