I doubt it would be too bad with respect to storing 0s. Unlike tensorflow you only have to pad up to the max length of the longest example in the batch. If it were really important to not pad too much for each batch you could sort your examples by length then create your batches. This is one common technique used to make RNN models more efficient
That being said, in at least the NLP models where I have used embeddings/embedding bags that part is quite fast compared to the rest of the model. I would recommend implementing the model, and if it is still too slow then optimize rather than worry about it right away