The foundation for MLCommons began with the MLPerf benchmark in 2018, which rapidly scaled as a set of industry metrics to measure machine learning performance and promote transparency of machine learning techniques. MLCommons is an open engineering consortium with a mission to benefit society by accelerating innovation in machine learning. “That rapid increase in performance will ultimately unleash new machine learning innovations that will benefit society.” “Looking back to the first MLPerf Training round in 2018, it’s remarkable that performance has improved by 30X for some of our benchmarks,” said David Kanter, Executive Director of MLCommons. It’s particularly exciting to see the advances in the Open Division.” “Congratulations to all of our participants in this round, especially the first-time submitters. “We’re thrilled to have such broad participation in MLPerf Training,” said Victor Bittorf, Co-Chair of the MLPerf Training Working Group. ![]() Submissions this round included software and hardware innovations from Azure, Baidu, Dell, Fujitsu, GIGABYTE, Google, Graphcore, HabanaLabs, HPE, Inspur, Lenovo, NVIDIA, Samsung, and Supermicro. The latest benchmark round received submissions from 14 organizations and released over 185 peer-reviewed results for machine learning systems spanning from edge devices to data center servers. MLPerf Training v1.1 results further MLCommons’ goal to provide benchmarks and metrics that level the industry playing field through the comparison of ML systems, software, and solutions. Submissions are additionally classified by availability within each division, including systems commercially available, in preview, and RDI. Closed submissions use the same reference model to ensure a level playing field across systems, while participants in the open division are permitted to submit a variety of models. ![]() Similar to past MLPerf Training results, the submissions consist of two divisions: closed and open.
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