Hi,
This is expected behavior I’m afraid.
Different machines will have different hardware/software stacks that can lead to very small differences in floating point ops results. When training a neural net, such error will usually be increased by the training process, leading to different final results.
But if your training is stable, it should converge to very similar loss function values.