The MLPerf consortium has recently released over 500 validated inference benchmarking results from 14 organizations measuring how fast and how well a pre-trained computer system can classify images, detect objects, and translate sentences. Over 400 of these results were submitted by dividiti, a high-tech company based in Cambridge, UK. "Our success in MLPerf Inference v0.5 is due to our unique open workflow automation technology called Collective Knowledge (CK)", explains Dr Anton Lokhmotov, CEO and co-founder of dividiti. "We conducted literally hundreds of benchmarking experiments, followed by thousands of auditing experiments, with many combinations of machine learning models, libraries, frameworks and hardware platforms. Such experiments are notoriously hard to stage in an automated, portable and reproducible fashion, which explains why even well-resourced hardware vendors only submit a handful of results. In collaboration with Arm and the Polytechnical University of Milan, we staged experiments on systems ranging from Raspberry Pi class boards and Android phones to high-end workstations. Benchmarking anything anywhere is what we call omni-benchmarking."