An AI workload is a computing task that uses artificial intelligence or machine learning models to process data, learn patterns, generate predictions, or produce outputs. It can include model training, inference, fine-tuning, data preprocessing, evaluation, and other operations that require substantial computational resources.
Different AI workloads place different demands on infrastructure. Training large models may require distributed GPUs and high-speed data movement, while inference workloads often prioritize low latency and efficient resource utilization. Understanding these requirements helps organizations select appropriate processors, accelerators, memory, storage, and cloud or on-premises infrastructure for AI applications.
