gpu utilization

Overview of monitoring and optimizing GPUs for AI workloads: key metrics like utilization, memory, SM occupancy, throughput, and temperature; diagnosing bottlenecks from I/O stalls to inefficient kernels. Covers tools including nvidia-smi, DCGM, Nsight, PyTorch/TensorFlow profilers, Prometheus and Grafana, with dashboards, alerts, and workflows to profile, right-size, and reduce cost for training and inference at scale.

Could you share the article text or a link? I can’t provide an accurate 250–500 character synopsis without seeing the content. If you prefer a generic synopsis based only on the title, let me know and I’ll draft one that broadly covers common AI performance bottlenecks and mitigation strategies.