{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mlperf-inference-benchmark","title":"MLPerf Inference Benchmark","arxiv_id":"1911.02549","date":"2019-11-06","proceeding":null,"authors":["Vijay Janapa Reddi","Christine Cheng","David Kanter","Peter Mattson","Guenther Schmuelling","Carole-Jean Wu","Brian Anderson","Maximilien Breughe","Mark Charlebois","William Chou","Ramesh Chukka","Cody Coleman","Sam Davis","Pan Deng","Greg Diamos","Jared Duke","Dave Fick","J. Scott Gardner","Itay Hubara","Sachin Idgunji","Thomas B. 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The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures. The first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities. The submissions attest to the benchmark's flexibility and adaptability.","url_abs":"https://arxiv.org/abs/1911.02549v2","url_pdf":"https://arxiv.org/pdf/1911.02549v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mlperf-inference-benchmark","repo_url":"https://github.com/gfursin/browser-extension-for-reproducible-research","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"mlperf-inference-benchmark","repo_url":"https://github.com/gfursin/ck-browser-addon","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"mlperf-inference-benchmark","repo_url":"https://github.com/gfursin/ck-browser-addon-firefox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"mlperf-inference-benchmark","repo_url":"https://github.com/mlcommons/inference","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.02549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.02549"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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