Better Artificial Intelligence for Everyone

Joined September 2020
MLPerf is the performance yardstick in the EU's AI Gigafactories tender, and MLPerf Endpoints v1.0 is open for submissions, with rolling results and mandatory power reporting. Blog: mlcommons.org/2026/10/open-s… #MLPerf #AIBenchmarks #AI
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MLPerf Client is in PCMag, StorageReview, and Wccftech, which used it to measure on-device AI performance on new laptops, including GenAI image generation and agentic workloads. Thanks to the reviewers! Try MLPerf Client: github.com/mlcommons/mlperf_…
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MLCommons retweeted
What is MLPerf Endpoints? It measures the served endpoint the way you actually buy it: model, hardware, serving software, and networking together, under production-like load. Guide: mlcommons.org/2026/10/mlperf… #MLPerf #AIInfrastructure #AIBenchmarks
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MLCommons has been selected as the Benchmarking Team for the STRIDE Ventures AI Efficiency Challenge. A $3.5 million, two-year award will fund MLPerf Agents: an industry-standard benchmark for the efficiency of complete agentic AI workflows. mlcommons.org/2026/10/nsf-st…
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What is MLPerf Endpoints? It measures the served endpoint the way you actually buy it: model, hardware, serving software, and networking together, under production-like load. Guide: mlcommons.org/2026/10/mlperf… #MLPerf #AIInfrastructure #AIBenchmarks
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Our mission, updated: standardize AI measurement and enable informed decisions. Read the letter from President Peter Mattson: mlcommons.org/2026/10/missio…
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A vendor's AI benchmark caught 82% of bugs. A rival re-ran the same test: 45%. An independent lab built its own: 63%. Same products. Only the grading changed. New from MLCommons: Assessing & Managing AI Risk for Business and Commercial Deployments. mlcommons.org/2026/10/ai-ris…
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A chatbot's data protection risks are, for the most part, limited to the individual user. For an agent, the scope is vast. MLCommons releases the Agent Privacy Risk Taxonomy v0.1 - five risk areas specific to agents. Read more: mlcommons.org/2026/10/agent-… #AIPrivacy #AgenticAI
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MLPerf Training introduces DLRMv4: HSTU sequence modeling + Yambda-5B (4.79B real interactions) + a 560 GB embedding footprint. Production-scale recommenders, now benchmarked. Full breakdown from the AMD, Meta, NVIDIA, and ByteDance task force: More: mlcommons.org/2026/10/dlrmv4…
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MLPerf Training v6.1 adds the suite's first LLM post-training benchmark: agentic RL that teaches a 397B-parameter open-weight model to repair real software, scored on pass@4 quality - not just throughput. Details from the task force: mlcommons.org/2026/09/mlperf…
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MLCommons joins the EU-funded AIRIS consortium to benchmark next-generation biomedical AI. We're building an evaluation framework to measure mechanism-informed generative AI across accuracy, robustness, fairness, interpretability, and usability. More: mlcommons.org/2026/09/join-a…
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MLPerf Inference v6.1 WG chairs Miro Hodak and Frank Han walk through the record round, new silicon (AMD MI350P, Intel Arc Pro B70, NVIDIA Vera Rubin), the first cross-vendor heterogeneous submission, and what it signals for inference engineering. mlcommons.org/2026/09/chairs…
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🧵 1/ @MLPerf Inference v6.1 is here. Record participation, new benchmarks, and substantial performance gains. 30 organizations submitted — the most ever. Details: mlcommons.org/2026/09/mlperf…
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AI Infra 2026: MLPerf Inference v6.1 - Technical deep dive. David Kanter, Miro Hodak (AMD), and Frank Han (Dell) on serving scenarios, reasoning workloads, edge measurements, and what's driving the biggest gains this round. Plus: a preview of MLPerf Endpoints. #AIInfraSummit
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