Overall, reducing the number of encoder and decoder layers decreases batch inference times and carbon footprints but impacts model accuracy when evaluated on similar training times. Additionally, changes in architecture are not directly compatible with KD of the original architecture, as indicated by the low top-1 and top-10 accuracy scores when using KD on alternative architectures. In conclusion, for single-step prediction, decreasing the size of the BART Chemformer variant appears more efficient (in accuracy, sample time, fraction of invalid) than employing knowledge distillation on any architecture variant.