The relative loss values depend on the used criterion, but overfitting a single batch should converge the loss to zero for a “standard” criterion.
If your model isn’t able to overfit a single sample, the overall training is ill-defined. I.e. the model architecture, hyperparameters, etc. could be “bad” and it’s hard to give a general statement as it depends on the use case.