Organization
MLCommons
MLCommons is a standards body and open-source steward based in Dover, Delaware19. This catalog lists 2 evaluation tools from MLCommons, under licenses including the Apache License 2.0 (ModelBench) and the Creative Commons Attribution 4.0 International (demo prompt sets).
Documented roles: Standards body · Open-source steward
MLCommons is an AI engineering consortium whose members include companies, academics, and nonprofits. It develops benchmarks, including the MLPerf performance suites and the AILuminate safety and security benchmarks, as well as open datasets and tools such as Croissant and MLCube. It launched in December 2020.12
Using this organization’s offerings
The detailed audience, self-hosting, pricing, and restrictions review has not yet been completed. Consult the sourced product records below and the publisher’s current terms. Missing detail means unknown, not unavailable.
Key facts
What is and isn’t public
Publishes MLPerf reference implementations and the ModelBench benchmarking framework in public GitHub repositories under Apache 2.0, plus 1,200-prompt AILuminate demo sets in English and French under CC BY 4.0. AILuminate's official test prompts are kept hidden, and its full practice prompt sets are offered to MLCommons members.5678
Products
No product records yet.
Cataloged artifacts
Evaluation tools
- AILuminateEvaluation toolAvailability: Partial
- MLPerfEvaluation toolAvailability: Public
Notable documented facts
- MLPerf is a registered trademark of MLCommons, and submitting benchmark results requires a signed Contributor License Agreement.2
- Under the MLPerf submission rules, submitting organizations review one another's results during a confidential review period before MLCommons publishes all results at once; after publication, code and results are free to use under the MLPerf Terms of Use.3
- The AILuminate benchmarks are developed by the MLCommons AI Risk & Reliability working group, which at the v1.0 release included researchers from universities, civil society representatives, and engineers from technology companies.4
In the news
MLPerf Training adds an LLM post-training benchmark, starting with round v6.1
MLCommons announced on September 24, 2026 that MLPerf Training is adding an LLM post-training benchmark, beginning with the v6.1 submission round in October 2026. The benchmark reports the wall-clock time a system needs to reach a quality target when post-training Qwen3.5-397B-A17B with reinforcement learning with verifiable rewards, using Group Relative Policy Optimization, on Python software engineering problems from the R2E-Gym dataset. Agents work in sandboxed Linux containers, and the specification and reference implementation are in the mlcommons/training repository.
MLCommons publishes MLPerf Inference v6.1 results with two new tests
MLCommons published results for version 6.1 of the MLPerf Inference benchmark suite on September 16, 2026. The round added two tests: an end-to-end retrieval-augmented generation test that runs a question-answering pipeline of several separate models, and an Edge Agentic Inference test built around multi-turn workloads such as agentic coding. Speculative decoding is now allowed in the interactive scenario for two of the inference benchmarks. Results are published on MLCommons' datacenter and edge results pages.
MLCommons publishes MLPerf Storage v3.0 results with KV cache and vector database tests
MLCommons published results for version 3.0 of its MLPerf Storage benchmark suite on September 1, 2026. The suite measures the performance of storage systems for machine learning workloads, and v3.0 adds a KV Cache test of storage performance for LLM inference cache read and write operations and a Vector Database test covering vector indexing and querying. Version 3.0 also adds support for an S3 object storage access layer alongside the existing POSIX layer, supported for training, checkpointing, and some vector database tests. Results are on the MLCommons Storage benchmark results page.
MLCommons releases MLPerf Client v2.0 with image generation and agentic AI tests
MLCommons released version 2.0 of MLPerf Client, its benchmark for running AI workloads locally on PCs, on August 18, 2026. The release adds an image generation category, with Flux.2 klein 4B as an experimental test, and an agentic AI category with Software Engineering Agent and Data Analyst Agent scenarios that break end-to-end results into LLM inference and tool execution time. Phi 4 Mini Instruct replaces Phi 3.5 mini instruct in the required LLM workloads, and Qwen 3 8B is added as an experimental test. MLCommons says v2.0 is free to download for Windows, macOS, and Linux from GitHub. USASI has not tested MLPerf Client.
MLCommons releases MLPerf Endpoints v0.7, a benchmark for buyers of hosted inference
On July 28, 2026, MLCommons announced MLPerf Endpoints v0.7, a foundation release of a new MLPerf benchmark aimed at enterprise buyers procuring inference compute from neoclouds, cloud providers, and managed services. The release covers three benchmarks with initial results from CoreWeave, Google, Intel, KRAI, and NVIDIA, and supports automated submission pipelines, continuous review tooling, and dynamic result visualization. Results and tooling are at endpoints.mlcommons.org. MLCommons says it will deliver MLPerf Endpoints v1.0 later in 2026, with more buyer-centric rules and agentic workloads.
What this catalog does not know
- Official careers page: not verified by this catalog.
- Products: none documented in this record.
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U.S. eligibility
Eligible · basis: U.S. nonprofit or lab
MLCommons Association lists a Dover, Delaware address on its About page, and the IRS exempt-organization extract for Delaware lists MLCommons Association at that address as a 501(c)(6) organization. Its members come from many countries; this assessment concerns the association itself.19
Sources
This listing is not an endorsement or a federal approval.
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APA style
USASI. (2026, October 1). MLCommons. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/companies/mlcommons/
BibTeX
@misc{usasi_mlcommons,
author = {{USASI}},
title = {MLCommons},
year = {2026},
month = oct,
howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/companies/mlcommons/}},
note = {United States of America Superintelligence. Last updated 2026-10-01}
}The date is when this page was last updated. Add the date you read it if your style needs one.
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