Solved by ptrblck in post #2 
              
              
                The losses should not get an NaN values, while the gradients might encounter NaNs, which will reduce the loss scaling. 
Could you check the models operations for custom ops, which might require FP32 precision due to overflow in FP16? 
Also, if you just want to save memory, you could try to use torch…