I assume you are already calling model.eval() in both cases.
If that’s the case, you could pass a constant tensor through both models and compare the output (e.g. use a torch.ones(size) tensor).
If the results are equal, your data loading or processing might differ in some ways.
On the other hand, if the results are different, you could check the intermediate activations of each layer via forward hooks.