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Submission: On May 27 via api from US — Scanned from DE
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* Skip to primary navigation * Skip to main content MLCommons Better Machine Learning for Everyone Menu * Benchmarks Submenu * Benchmarks * AI Safety Benchmarks * MLPerf Training * MLPerf Training: HPC * MLPerf Inference: Datacenter * MLPerf Inference: Edge * MLPerf Inference: Mobile * MLPerf Inference: Tiny * MLPerf Storage * Datasets Submenu * Datasets * Cognata Dataset * Dollar Street * Multilingual Spoken Words * People’s Speech * Working Groups Submenu * Working Groups * Benchmarks * AI Safety * Data * Research * AI Safety * Research * About Us Submenu * About Us * Leadership * Programs * Policies * Blog * Join Us * Search 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. Get involved 125+ MLCommons Members and Affiliates 6 Benchmark Suites 55,000+ MLPerf Performance Results to-date ACCELERATING ARTIFICIAL INTELLIGENCE INNOVATION In collaboration with our 125+ founding members and affiliates, including startups, leading companies, academics, and non-profits from around the globe, we democratize AI through open industry-standard benchmarks that measure quality and performance and by building open, large-scale, and diverse datasets to improve AI models. About us FOCUS AREAS We help to advance new technologies by democratizing AI adoption through the creation and management of open useful measures of quality and performance, large scale open data sets, and ongoing research efforts. BENCHMARKING Benchmarks help balance the benefits and risks of AI through quantitative tools that guide effective and responsible AI development. They provide consistent measurements of accuracy, safety, speed, and efficiency which enable engineers to design reliable products and services and help researchers gain new insights to drive the solutions of tomorrow. Learn more DATASETS 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. Learn more RESEARCH Open collaboration and support with the research community helps accelerate and democratize scientific discovery. MLCommons shared research infrastructure for benchmarking, rich datasets and diverse community, help enable the scientific research community to derive new insights for new breakthroughs in AI, for the betterment of society. Learn more MEMBERS MLCommons is supported by our 125+ founding members and affiliates, including startups, leading companies, academics, and non-profits from around the globe. MEMBERS FOUNDING MEMBERS MEMBERS FOUNDING MEMBERS MEMBERS 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! Get involved FEATURED ARTICLES April 17, 2024 News MLPERF TINY V1.2 RESULTS MLPerf Tiny results demonstrate an increased industry adoption of AI through software support April 16, 2024 News ANNOUNCING MLCOMMONS AI SAFETY V0.5 PROOF OF CONCEPT Achieving a major milestone towards standard benchmarks for evaluating AI Safety April 16, 2024 Blog THE AI SAFETY ECOSYSTEM NEEDS STANDARD BENCHMARKS IEEE Spectrum contributed blog excerpt, authored by the MLCommons AI Safety working group March 27, 2024 News NEW MLPERF INFERENCE BENCHMARK RESULTS HIGHLIGHT THE RAPID GROWTH OF GENERATIVE AI MODELS With 70 billion parameters, Llama 2 70B is the largest model added to the MLPerf Inference benchmark suite. March 27, 2024 Blog LLAMA 2 70B: AN MLPERF INFERENCE BENCHMARK FOR LARGE LANGUAGE MODELS MLPerf Inference task force shares insights on the selection of Llama 2 for the latest MLPerf Inference benchmark round. March 10, 2023 Blog PERSPECTIVE: UNLOCKING ML REQUIRES AN ECOSYSTEM APPROACH Factories need good roads to deliver value See all blogs and news SUBSCRIBE FOR THE LATEST NEWS Email(Required) Consent(Required) I agree to the privacy policy. By submiting this form I agree with the Privacy Policy CAPTCHA * Legal * Policies * Privacy Policy * Quick Links * Calendar * Discord * Github * Contact * support@mlcommons.org * Follow Us © 2020–2024 MLCommons Search this website ˄ Close Notifications