MLCommons
Articles about MLCommons
Assessing & Managing AI Risk for Business and Commercial Deployments: A business owner’s guide to a cost you can measure, price, and manage
AI risk is already on your books. A business owner’s guide to measuring, pricing, and managing it with independent benchmarks like AILuminate
What Could Go Wrong When Agents Handle Your Sensitive Data? A New Taxonomy Answers
MLCommons’ Privacy Working Group releases v0.1 of the Agent Privacy Risk Taxonomy: five areas of data protection risk unique to AI agents, from data ingestion to accountability and governance.
DLRMv4: The Next-Generation Recommendation MLPerf Training Benchmark
MLPerf Training introduces DLRMv4, a next-generation recommendation benchmark built on HSTU sequence modeling and the Yambda-5B dataset with a 560 GB production-class embedding footprint.- Focus keyphrase: DLRMv4 MLPerf Training
MLPerf Training v6.1: First LLM Post-Training Benchmark
MLPerf Training v6.1 adds an agentic RL benchmark: teach a 397B-parameter model to repair real software. Reference run: 256 Blackwell Ultra GPUs.
MLCommons Joins EU-Funded AIRIS Project to Build and Benchmark Next-Generation Biomedical AI
Over the summer, MLCommons was honored to be announced as a consortium member in a new research project AIRIS, (Mechanism-Informed Multimodal Generative AI for Causal and Dynamical Modeling in Biomedical Research), funded by the European Union’s Horizon Europe Program.
Where the Industry Is Investing: A Look at MLPerf Inference v6.1
MLPerf Inference Working Group chairs Miro Hodak and Frank Han analyze the broadest round yet — 30 submitters, 120 systems, new agentic benchmarks, and record performance leaps.
MLCommons Sets Participation Record with New MLPerf Inference v6.1 Benchmark Results
Latest version of the benchmark introduces two new tests for emerging AI deployment patterns, including Agentic Inference