Despite the significant utility of GPUs in AI/ML workloads, their high cost necessitates robust observability and management strategies to optimize performance and cost efficiency.
Monitoring GPU memory utilization is crucial as it can significantly impact the training speed of models by ensuring that data processing occurs efficiently in memory.
Understanding the role of GPU cores is essential for optimizing ML workloads, as they are responsible for executing the complex matrix operations crucial to model training.
Effective accumulation of GPU-level metrics allows engineering teams to make informed decisions that maximize resource utilization and enhance the overall AI/ML lifecycle.
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