Thomas, Thanks for this comparison point.  If you’re getting ~95ms on a laptop, that tells me the 200ms deadline is not unreasonable and executing above that level does flag a possible performance concern.  Now in this case, the execution environment is in a conda env within a Ubuntu docker container on the OSUOSL lab on a shared server, so it’s not an environment optimized for performance.
In fact, this is literally the exact ppc64le CI which is directly linked to from the pytorch github home page README.md labelled as 3.6 and “Linux (ppc64le) GPU”, which points to the CI at: https://powerci.osuosl.org/job/pytorch-linux-cuda92-cudnn7-py3-mpi-build-test-gpu/ 1  (in spite of misleading URL name, it’s on cuda10; we’ll fix that URL once it is running cleanly).
Anyhow, this testcase appears new as of pytorch 1.3 and I’m going to try to get a build running and tested on our own local systems to see how the execution of this test compares.  (Of course, we’d then like to get the public CI version showing a successful test completion too.)