Just run their benchmarks. Submitting your results there is a bit more complicated, because all results there are "verified" by independent entities.
If you feel like your AI use case is not well represented by any of the MLPerf benchmarks, open a discussion thread about it, propose a new benchmark, etc.
The set of benchmarks there increases all the time to cover new applications. For example, on top of the MLPerf Training and MLPerf Inference benchmark suites, we now have a new MLPerf HPC suite to capture ML of very large models.
We do: https://mlperf.org/
Just run their benchmarks. Submitting your results there is a bit more complicated, because all results there are "verified" by independent entities.
If you feel like your AI use case is not well represented by any of the MLPerf benchmarks, open a discussion thread about it, propose a new benchmark, etc.
The set of benchmarks there increases all the time to cover new applications. For example, on top of the MLPerf Training and MLPerf Inference benchmark suites, we now have a new MLPerf HPC suite to capture ML of very large models.