MLPerf Inference Delivers Power Efficiency and Performance Gains

MLCommons, the leading open AI engineering consortium, announced new results from the industry-standard MLPerf Inference v3.0 and Mobile v3.0 benchmark suites, which measure the performance and power-efficiency of applying a trained machine learning model to new data. The latest benchmark results illustrate the industry's emphasis on power efficiency, with 50% more power efficiency results, and significant gains in performance by over 60% in some benchmark tests. Inference is the critical operational step in machine learning, where a trained model is deployed for actual use, bringing intelligence into a vast array of applications and systems. Machine learning inference is behind everything from the latest generative AI chatbots to safety features in vehicles such as automatic lane-keeping, and speech-to-text interfaces. Improving performance and power efficiency will lead the way for deploying more capable AI systems that benefit society.