If you don't have 8 GPUs you can always run the same computation 8x in series on one GPU. Then you merge the results the same way the parallel implementation would do it. In most cases that's probably gonna end up being a form of gradient accumulation. Think of it this way: you basically compute your distances on a subset of n, but since there are much fewer pairs of distances, the gradient would be noisy. So you just run it a couple of times and average the result to get an approximation of the real thing. Very likely that this is what the parallel implementation does too.