Here, we address the efficiency issue of a retrosynthesis Transformer model by employing alternative architectures, KD and hyper-parameter optimization. We then analyze the impact these optimizations have on the fine-tuned models in terms of accuracy, speed and carbon footprint. Importantly, we investigate whether single-step speed-up translates into multi-step retrosynthesis search times. This work thus demonstrates the promise for scaling up a retrosynthesis transformer model to reduce carbon footprints while retaining prediction accuracy and multi-step solvability.