Better AI for Everyone
Building trusted, safe, and efficient AI requires better systems for measurement and accountability. MLCommons’ collective engineering with industry and academia continually measures and improves the accuracy, safety, speed, and efficiency of AI technologies.
Our Members
MLCommons is supported by over 125 members and affiliates, including startups, leading companies, academics, and non-profits from around the globe.
By the numbers
Accelerating AI Innovation
At MLCommons, we democratize AI through open industry-standard benchmarks that measure quality, performance, and risk. And though our work building open, large-scale, and diverse datasets to improve AI models.
125 +
MLCommons Members and Affiliates6
Benchmark Suites57 k
MLPerf Performance Results to-dateWhat We Do
Performance Benchmarks
Benchmarks help balance the benefits and risks of AI through quantitative tools that guide responsible AI development. They provide neutral, consistent measurements of accuracy, speed, and efficiency which enable engineers to design reliable products and services, and help researchers gain new insights to drive the solutions of tomorrow.
AI Risk & Reliability
The MLCommons AI Risk & Reliability working group is composed of a global consortium of AI industry leaders, practitioners, researchers, and civil society experts committed to building a harmonized approach for safer AI.
Data & Research
Evaluating AI systems depends on rigorous, standardized test datasets. MLCommons builds open, large-scale, and diverse datasets and a rich ecosystem of techniques and tools for AI data, helping the broader community deliver more accurate and safer AI systems.
Our shared research infrastructure and diverse community aid the scientific research community to derive new insights for new breakthroughs in AI.
Community
Community-driven and funded
We’re a collective of data nerds, AI experts, and enthusiasts who are passionate about accelerating AI. While data, modeling and all that good stuff is critical, it’s the people behind it all that are the bedrock of MLCommons.
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