An AI inference benchmark can measure throughput, latency, energy use, memory consumption, cost, or combinations of those quantities. It should specify the model, batch behavior, precision, request pattern, kernels, and target quality.
A complete suite is more reliable than one favorable test because serving tradeoffs differ across workloads. Preliminary results should remain provisional until preferred methods and independent checks are finished.
ELI5
An inference benchmark measures how well a system runs an AI model after training. It can compare response time, requests per second, memory use, energy, cost, or several of those together.
For example, two servers can run the same model and answer the same test requests while the benchmark records speed and power use. A fair comparison keeps the model, answer quality, batch size, and software conditions consistent.
What should an inference benchmark report?
It should report the model, hardware, software, precision, batch size, request pattern, quality target, latency, throughput, power boundary, and test procedure.
Why can inference benchmark results vary?
Kernels, compiler versions, batching, caching, quantization, traffic shape, power limits, and measurement choices can substantially change the result.
