What GPU Metrics to Monitor and Why?
With the increasing reliance on GPUs for compute-intensive tasks such as machine learning, deep learning, data processing, and rendering, both infrastructure administrators and users of GPUs (i.e. data scientists, ML engineers and GenAI app developers) require timely access and insights into performance, efficiency, and overall health of their GPU resources.
In order to make data driven, logical decisions, it is critical for these users to have access to critical metrics for their GPUs. This is the first blog in a series where we will describe the GPU metrics that you should track and monitor. In subsequent blogs, we will do a deep dive into each metric, why it matters and how to use it effectively.
Important
Navigate to documentation for Rafay's integrated capabilities for Multi Cluster GPU Metrics Aggregation & Visualization.
