It could help if the OOM is triggered by memory fragmentation caused by allocating increasingly larger tensors, e.g. via:
for size in torch.arange(1, 100):
    x = torch.randn(size, device='cuda')

as the larger tensors won’t be able to directly reuse the already allocated memory of the smaller one.
Reversing the order, i.e. starting with the largest tensor, would fix this.
With that being said, I would claim it depends on your use case and why you are running OOM or what is causing the fragmentation in the first place and what exactly you would like to pre-allocate.