Which action is likely to improve GPU utilization in a multi-GPU server training deep learning models?

Increasing the batch size is a likely effective action to improve GPU utilization in a multi-GPU server when training deep learning models. When you increase the batch size, you are providing more data for the GPUs to process in parallel during each training iteration. This can lead to better utilization of the GPUs' computational resources, as they have more work to do concurrently.

In a multi-GPU setup, it’s essential to keep the GPUs busy to fully leverage their combined processing power. A larger batch size means that more inputs are processed at once, which can help reduce idle times between batch processing and ensure that the GPUs are continuously working. Additionally, larger batch sizes can help reduce communication overhead between GPUs as the combined processing of larger batches may lead to fewer synchronization points.

On the other hand, updating the CUDA version may optimize performance but doesn't directly influence how much the GPUs are utilized in the context of your training model. Disabling NVLink for PCIe communication reduces the efficiency of data transfer between GPUs, potentially lowering overall performance. Optimizing code for mixed-precision training can also be beneficial but is more about enhancing speed and memory usage efficiency, rather than directly increasing GPU utilization. Therefore, increasing the batch size is a straightforward method to enhance GPU utilization in a deep