Organizations from a wide array of industries depend on artificial intelligence (AI) and machine learning (ML) to forecast sales, segment their customer bases, identify risks, manage complex supply networks, optimize costs, and improve efficiencies. However they are applied, all AI and ML use cases depend on compute performance (in addition to the speed and quantity of memory and the bandwidth of interconnects and networking). And due to the nature of the data companies use for activities like predictive customer analytics or fraud detection, security is often just as critical. Because of the performance needs of AI and ML, the infrastructure from compute, local memory, network bandwidth, and data storage to support these workloads can represent a significant investment, which drives the need for rigorous evaluation before purchase. Industry-standard benchmarks can be good for this—and world records can be even better—if they are evaluated in the right way.
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