The new set of MLPerf results proves it

The days and sometimes weeks it took to train AIs only a few years ago was a big reason behind the launch of billions of dollars-worth of new computing startups over the last few years—including Cerebras Systems, Graphcore, Habana Labs, and SambaNova Systems. In addition, Google, Intel, Nvidia and other established companies made their own similar amounts of internal investment (and sometimes acquisition). With the newest edition of the MLPerf training benchmark results, there’s clear evidence that the money was worth it.

The gains to AI training performance since MLPerf benchmarks began “managed to dramatically outstrip Moore’s Law,” says David Kanter, executive director of the MLPerf parent organization MLCommons. The increase in transistor density would account for a little more than doubling of performance between the early version of the MLPerf benchmarks and those from June 2021. But improvements to software as well as processor and computer architecture produced a 6.8-11-fold speedup for the best benchmark results. In the newest tests, called version 1.1, the best results improved by up to 2.3 times over those from June. Read More