What is the most likely cause of high memory usage but low compute usage in GPU nodes?

The most likely cause of high memory usage but low compute usage in GPU nodes is that the data being processed includes large datasets that are stored in GPU memory but not efficiently utilized in computation. This scenario typically occurs when the model architecture or the processing pipeline requires large amounts of data to be loaded into the GPU memory, but the computations performed on that data do not sufficiently utilize the available GPU processing power. This can happen in situations where data is being preloaded for potential use, but the actual computation performed is limited due to inefficiencies in the model or processing framework.

For example, if a model is designed to process images but only a subset of those images are being utilized in any single computation cycle, much of the data in memory goes unused, resulting in high memory usage without a corresponding increase in compute workload. Properly optimizing data loading and computation strategies is essential to balance these two aspects effectively, ensuring that the GPU's capabilities are being fully utilized.