Neural networks have two different compute costs: training and inference.

These are roughly analogous to compile time vs runtime for compiled programming languages.

Training is in general a more intensive task. However, in an ideal scenario training is run once and inference is run millions of times, so the lifetime cost of inference is bigger - this is why it might make sense to optimize for intense.