If model A reaches performance level 100 using 100 units of compute using old methods, and you train model B using AttnRes, aiming at performance level 100, it costs you 80 units of compute.

It probably doesn't map precisely, but that's where people are diverging from the claim - it doesn't explicitly say anything about reduced inference or training time, but that's the implicit value of these sorts of things. Less compute to equivalent performance can be a huge win for platforms at scale as well as for local models.