Capex is a promise; tuning is one of the hidden costs that decides whether it's kept. NVIDIA's grant US11100643B2 (“Training strategy search using reinforcement learning,” issued 2021-08-24) attacks that hidden cost. Assigned to NVIDIA Corporation and classified CPC G06N 3/0454, it uses reinforcement learning to automatically search for good training configurations instead of having engineers hand-tune them.
The cost being attacked is real but invisible on a balance sheet. Getting a model to train well requires choosing among many configurations, and doing it manually burns both engineer time and wasted compute on dead-end runs. Automating that search compresses the path from raw silicon to a working model — which is, in financial terms, a cost reduction.
“In at least one embodiment, a reinforcement-learning-based searching approach is used to produce a training configuration for a machine-learning model. In at least one embodiment, 3D medical image segmentation is performed using learned image preprocessing parameters.”— U.S. Patent No. 11,100,643 source
NVIDIA's Data Center segment dominates its income statement, and the company's value rests partly on making the full AI pipeline efficient, not just on selling chips. This patent is one piece of that pipeline IP: the part that reduces the human and compute overhead of getting from hardware to a trained model.
I'll resist a number, because the patent doesn't support one and no NVIDIA filing isolates tuning cost. What the grant documents is that the automation of an expensive, easy-to-overlook step was being patented in 2021, while the Data Center revenue that it ultimately supports was still climbing.
The reusable point for the capex desk: payback depends not only on chip efficiency but on how cheaply the whole workflow turns compute into capability. Automated tuning IP is one of those workflow levers, and NVIDIA owning a dated grant on it is part of why its position is more than a hardware position.