That’s what I was referring to.
You can expect bigger speedups, if you use the TensorCores in your V100 (FP16 ops, shapes of multiples of 8 for GEMMs), while other use cases might give a lower speedup or in the worst case none at all, if the bottleneck is e.g. the data loading.
If you are using some heavy preprocessing and data loading, this might create the major botteneck in your code. While the V100 could still be faster than the P100 in the training loop, both would have to wait for the next batch to be ready, so that the performance benefit of your V100 will be hidden in the waiting time.