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The Financial Services Working Group's flagship initiative advances to the final round of the C:>DIR Global 'Agentic Regulator' Hackathon, with winners announced September 18. The post MLCommons Agent

MLCommons announced the release of MLPerf Storage v3.0 benchmark results, expanding the suite to cover the full spectrum of AI storage workloads, including an S3 object‑storage access layer in additio

At Google Cloud Next 2026 in Las Vegas, MLCommons and Google Cloud demonstrated a powerful new capability for trustworthy medical AI - one that protects patient data, model IP, and benchmark integrity

Evolving MLPerf Training from feature-interaction baselines to production-scale sequential recommendation with HSTU and the 560 GB Yambda-5B embedding benchmark. The post DLRMv4: The Next-Generation R

MLPerf Training v6.1 adds an agentic reinforcement-learning workload that measures how quickly systems can teach a 397-billion-parameter language model to repair real software projects. The post MLPer

In this analysis, MLPerf Inference Working Group chairs Miro Hodak and Frank Han break down a record field of 30 submitters and 120 systems, the arrival of agentic and end-to-end benchmarks, and what

Benchmarking Retrieval-Augmented Generation End to End — from Building the Vector Database to Serving an Iterative, Multi-Hop Question-Answering Inference Pipeline The post Introducing the MLPerf End-

New benchmark categories for generative AI and agentic workflows arrive alongside updated LLM tests, as the industry standard for measuring PC AI performance evolves. The post MLPerf Client v2.0 Expan

The intersection of medicine and AI has led to remarkable innovations. However, developers now face the thorny challenge of building robust medical AI tools that have been tested and evaluated on dive

In this post, you learn how to prompt Amazon Nova 2 Lite for content moderation using structured and free-form approaches, grounded in the MLCommons AILuminate Assessment Standard. The prompting techn

TensorRT Edge‑LLM ran Qwen 3.6‑27B on a single NVIDIA Jetson AGX Thor and completed the MLPerf Edge Agentic benchmark in 24 min 36 s—52.33 tokens/s—which is 6.4× faster than the llama.cpp reference (2

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