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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
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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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
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…
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
Our mission, updated: standardize AI measurement and enable informed decisions.
Read the letter from President Peter Mattson: mlcommons.org/2026/10/missio…
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…
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
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…
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…
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…
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…
🧵 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…
6/ Orrick, @QuantaQCT, @RedHat, @scitix, @Supermicro, @withtta, VibeHPC, @WiwynnCorp, @NaeemKhoshnevis.
7/ 50%+ used our new API-centric harness — the LoadGen++ foundation for MLPerf Endpoints. The transition is happening.
mlcommons.org/2026/09/mlperf…
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












