Commissioned In just about any situation where you are making capital investments in equipment, you are worried about three things: performance, price/performance, and total cost of ownership. Without some sort of benchmark on which to gauge performance and without some sense of relative pricing, it is impossible to calculate total cost of ownership, and therefore, it is impossible to try to figure out what to invest the budget in.

This is why the MLPerf benchmark suite is so important. MLPerf was created only three and a half years ago by researchers and engineers from Baidu, Google, Harvard University, Stanford University, and the University of California Berkeley and it is now administered by the MLCommons consortium, formed in December 2020. Very quickly, it has become a key suite of tests that hardware and software vendors use to demonstrate the performance of their AI systems and that end user customers depend on to help them make architectural choices for their AI systems.

Next Platform “Why the MLPerf Benchmark is good for AI, and good for you.”

The MLPerf site can be found at https://mlcommons.org/en/