The differences you are seeing are approx. 1e-6 which comes most likely due to the limited floating point precision of float32.
The order of operations might yield different results as seen here:
x = torch.randn(10, 10, 10)
s1 = x.sum()
s2 = x.sum(0).sum(0).sum(0)
print((s1 - s2).abs().max())
> tensor(3.8147e-06)

I assume the difference in CPU architecture between both posted CPUs might explain the difference, but that’s just by best guess.
Also, the implementation of the pseudo-random number generator might be different for different hardware devices (e.g. CPU vs. GPU).