MLCommons and MLPerf – An Introduction

MLCommons is a non-profit industry consortium dedicated to improving AI for everyone by focusing on accuracy, safety, speed, and power efficiency. The organization boasts over 125 members across six continents and leverages community participation to achieve its goals. A key project is MLPerf, an open industry standard benchmark suite for measuring the performance and efficiency of AI systems, providing a common framework for comparison and progress tracking. This transparency fosters collaboration among researchers, vendors, and customers, driving innovation and preventing inflated claims.

The presentation highlights the crucial relationship between big data, big models, and big compute in achieving AI breakthroughs. A key chart illustrates how AI model performance significantly improves with increased data, but eventually plateaus. This necessitates larger models and more powerful computing resources, leading to an insatiable demand for compute power. MLPerf benchmarks help navigate this landscape by providing a standardized method of measuring performance across various factors including hardware, algorithms, software optimization, and scale, ensuring that improvements are verifiable and reproducible.

MLPerf offers a range of benchmarks covering diverse AI applications, including training, inference (data center, edge, mobile, tiny, and automotive), storage, and client systems. The benchmarks are designed to be representative of real-world use cases and are regularly updated to reflect technological advancements and evolving industry practices. While acknowledging the limitations of any benchmark, the presenter emphasizes MLPerf’s commitment to transparency and accountability through open-source results, peer review, and audits, ensuring that reported results are not merely flukes but can be validated and replicated. This approach promotes a collaborative, data-driven approach to developing more efficient and impactful AI solutions.