The thing about ML is that memory helps everything.

Take almost any given model that fits in X memory pool for some task. Double that, and everything gets better. Training quality goes up, inference quality potentially goes up, you gain the capacity for more batching which dramatically increases performance. Maybe more caching, depending on the app. You can add more models to the pipeline without much fuss.