mlperf

Overview of AI hardware benchmarks: what they measure; how to read throughput, latency, and energy metrics across training and inference; and how batch size, precision, memory bandwidth, and software stacks skew results. It contrasts data‑center and edge devices, flags pitfalls of synthetic tests, and offers tips for selecting hardware by model, budget, and deployment constraints.