You're in charge of a data science team within a large international corporation. Your team primarily develops large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. The typical iteration time for a new model version ranges from a few weeks to several months. Recently, there has been a request to assess and reduce your team's Google Cloud compute costs while ensuring that the model's performance remains unaffected. How can you achieve this cost reduction without compromising the model's quality?