As a Data Scientist at a rapidly growing startup, you are tasked with optimizing the training performance of deep learning models developed in Python using TensorFlow. After implementing caching and prefetching techniques, you decide to leverage GPUs on a single machine to further enhance performance, keeping in mind the constraints of cost efficiency and the need for a flexible environment for experimentation. The startup's projects vary in scale and complexity, requiring a solution that can efficiently utilize multiple GPUs without the overhead of distributed training across multiple machines. Which of the following strategies should you choose to best meet these requirements? (Choose one correct option)