The GA102 whitepaper 19 seems to indicate that the RTX cards do support bf16 natively (in particular p23 where they also state that GA102 doesn’t have fp64 tensor core support in contrast to GA100).
So in my limited understanding there are broadly three ways how PyTorch might use the GPU capabilities:

Use backend functions (like cuDNN, cuBlas) and hopefully they use all the latest and greatest.
When using intrinsics directly 3 conventional wisdom (see this 2017 paper discussing half vs. half2 performance 9) seems to say that bfloat162 will offer better performance over using bfloat16 unless the compiler has learned lots of new tricks. But I am not aware if we actually use that a lot in PyTorch.

Again, I’m still looking into how get most fp16 performance, so take this with a grain of salt.
Best regards
Thomas