To prevent out-of-memory errors on NVIDIA GPUs during large model execution, which metric is critical?

In the context of executing large models on NVIDIA GPUs, GPU Memory Usage is the most critical metric to monitor in order to prevent out-of-memory errors. This metric reflects the amount of GPU memory (VRAM) that is currently being utilized by the model and other processes. High GPU memory usage can lead to situations where the required data cannot fit into the available memory, resulting in out-of-memory errors.

When working with large models, if the GPU's memory usage approaches the total memory capacity, it indicates that there may not be enough space to load additional data or perform necessary computations. By keeping a close eye on GPU Memory Usage, you can take proactive measures such as optimizing the model or batching input data to avoid exceeding memory limits.

The other metrics, while important for overall GPU performance and efficiency, do not directly address the issue of memory availability in the same way. For instance, Power Usage is related to the energy consumption of the GPU, PCIe Bandwidth Utilization pertains to data transfer speeds between the GPU and other peripherals, and GPU Core Utilization indicates how effectively the processing units are being utilized. While they can provide valuable insights into GPU performance, they are not as directly linked to preventing out-of-memory errors as GPU Memory Usage is.