To improve GPU utilization and reduce training time for large language models, which action is most effective?

Using mixed precision training is a highly effective approach for improving GPU utilization and reducing training time, particularly for large language models. This technique involves using lower precision formats (such as float16) alongside traditional higher precision formats (like float32) during the training process.

By leveraging mixed precision, you can significantly reduce memory usage, which allows for larger batch sizes to be processed concurrently on the GPU. Larger batch sizes often lead to better accelerator utilization, meaning the GPU can perform more calculations simultaneously without idling. Additionally, operating with lower precision often speeds up computations due to the reduced data size, which can shorten the overall training duration.

This approach strikes a balance between maintaining model accuracy and enhancing performance, enabling trainees to efficiently utilize their available resources while still achieving the desired outcomes for large and complex models.

In contrast, increasing the batch size alone may not always lead to better performance if memory limitations are encountered, and while decreasing model complexity can lead to shorter training times, it may negatively impact the model's performance and capability. Reducing the learning rate can stabilize training but does not inherently improve efficiency or GPU utilization, and can sometimes lead to longer training times as convergence might happen more slowly.