Our Five Key Principles

01

Grow AI markets and make the world a better place

02

Get everyone involved

  • Be global, inclusive, and fair
  • Bring together academia, small companies, large companies, non-profits, etc.
  • Make it easy to get involved
  • Be as open with our IP as possible while sustaining the community

03

Act through collaborative engineering

  • Keep leadership mostly technical, with an emphasis on hands-on-involvement
  • Favor data-driven decisions, design simplicity, and focus on real user value

04

Make fast but consensus-supported decisions

  • Very low barrier for โ€œexperimentalโ€ working groups with well reviewed path to full endorsement
  • Favor grudging consensus over 51/49 votes, especially for big decisions
  • Make technical contributions easy
  • Favor rapid development and iteration

05

Build a community that people want to be part of

  • Be welcoming, informal, and friendly
  • Encourage, recognize, and reward contributions
  • Celebrate with cake

Our Mission

Our mission is to accelerate artificial intelligence innovation and increase its positive impact on society. In collaboration with our 125+ founding Members and Affiliates, including startups, leading companies, academics, and non-profits from around the globe, we democratize machine learning through open industry-standard benchmarks that measure quality and performance and build open, large-scale, and diverse datasets to improve AI models. 

Every major technological advance follows a similar trajectory towards universal adoption and impact. The arc from research to broad accessibility generally takes from 30-40 years: from early automobiles to the family car, from development of ARPANET to the mainstream World Wide Web, from the first cellular phones to an smartphone in every pocket. Each of these examples started with technological breakthroughs, but for decades was limited by expertise, access, and expense. 

Machine learning and artificial intelligence are no different. ML and AI have been being developed for decades, but even with todayโ€™s rapid advances, the technology continues to be fragmented, bespoke, and poorly understood. We believe that we can unlock the next stage of AI adoption by creating useful measures of quality and performance, by delivering large scale open data sets, and by advancing the state of the art through collaborative, open research that will lead to the next great ideas. These focus areas are aimed to help democratize AI and enable its widespread incorporation into new products and services in fields such as health, safety, and communication that deliver AI benefits to everyone. 


Our History

2018

February

Initial meetings between engineers and researchers from Baidu, Google, Harvard University, Stanford University, and the University of California Berkeley

2018

May 2

Launched the the MLPerf Training benchmark suite

2018

December 5

Launched the MLPerf HPC benchmark suite

2018

December 12

Published results from the first MLPerf Training benchmark suite, including results from Google, Intel, and NVIDIA

2019

June 24

Launched the MLPerf Inference benchmark suite

2019

October 22

Launched the TinyML benchmark suite

2019

November 6

Published results from the first MLPerf Inference benchmark suite, including results from Alibaba, Centaur Technology, Dell EMC, dividiti, FuriosaAI, Google, Habana Labs, Hailo, Inspur, Intel, NVIDIA, Polytechnic University of Milan, Qualcomm, and Tencent

2019

November 8

MLCube created

2020

January

Peopleโ€™s Speech kickoff

2020

April

First 10,000 hours of data for Peopleโ€™s Speech

2020

September

Complete prototype of MLCube. 80,000 hours of aligned data for Peopleโ€™s Speech

2020

October 21

First results using the MLPerf Mobile suite including results from Intel, MediaTek, Qualcomm, and Samsung

2020

November

Peopleโ€™s Speech shared with early adopters

2020

November 18

First results using the MLPerf HPC Training results, including results from the Swiss National Supercomputing Center (CSCS), Fujitsu, Lawrence Berkeley National Laboratory (LBNL), National Center of Supercomputer Applications (NCSA), Japanโ€™s Institute of Physical and Chemical Research (RIKEN), and Texas Advanced Computing Center (TACC)

2020

December 3

MLCommons launches

2023

November

MLCommons launches the AI Risk & Reliability working group with the mission of building a harmonized approach to safer AI. The working group is tasked with creating a platform, tools, and tests for developing a standard benchmark to measure the safety of AI.

Community

Join our community

MLCommons is a community-driven and community-funded effort. We welcome all corporations, academic researchers, nonprofits, government organizations, and individuals on a non-discriminatory basis. Join us!

Contact Us

MLCommons
3855 SW 153rd Drive
Beaverton, OR 97003