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Overview
MLCommons is an engineering consortium founded in 2020 that builds the benchmarks and datasets the machine learning industry measures itself against. It maintains MLPerf, the standard suite for training and inference performance, along with open speech datasets and safety benchmarks for language models. Its members include chip makers, cloud providers, model developers and academic groups.
News
Introducing the MLPerf End-to-End RAG Inference Benchmark
The MLCommons MLPerf Inference Working Group is excited to introduce the first instance of a new End-to-End Retrieval-Augmented Generation (RAG) benchmark. By answering from documents retrieved at query time rather than from weights alone, RAG reduces hal
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MLPerf Client v2.0 Expands AI PC Benchmarking with Image Generation and Agentic AI
MLCommons, an open engineering consortium dedicated to improving machine learning performance and transparency, today announced the release of MLPerf Client v2.0, the latest version of its industry-standard benchmark for evaluating AI performance on perso
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How to Tell When a Benchmark Is Worth Trusting
If you work in enterprise AI, you've probably been here: a vendor claims their model leads on a popular leaderboard. A procurement team is weighing two systems and benchmark scores are the tiebreaker. An executive deck cites a safety benchmark to argue a
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MLPerf Endpoints v0.7: A Foundation Release
MLPerf has been the standard-bearer for measuring AI system performance since its launch in 2018. During that time, MLPerf has tracked over a 100X improvement in inference performance per watt for large language models and over a 50X improvement in traini
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MedPerf Meets Google Cloud Confidential Computing: Secure AI Benchmarking for Brain Tumor Research
During Google Cloud Next 2026 in Las Vegas, the MLCommons Medical AI working group and Google Cloud announced the enablement of MedPerf, MLCommons' federated benchmarking orchestrator, on Google Cloud's confidential compute capabilities. Both teams demons
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