Hi, @vmacheret  No worries  Regarding, the marketing - I would say they are some special cases with all the highly optimized techniques from smart Nvidia engineers you can imagine. So, it is rarely the case one can see the x4 - x6 speedup in real life.
Back to the point. Resnet18 is still only the toy example. I believe mixed precision would shine on heavy GPU tasks (given that all CPU and IO bottlenecks are resolved). One has to have in mind that amp training is reducing the memory footprint, so you actually can try to train with a larger batch size, which imply you can iterate one epoch of your data faster. My suggestion: always try to utilize GPU compute as close to 100% as possible. It is how you make sure the most expansive part of your build doesn’t just stay idle waiting for the data.