Guenther Schmuelling

TL;DR: This paper presents the benchmarking method for evaluating ML inference systems, MLPerf Inference, and prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures.

TL;DR: The design choices behind MLPerf, a machine learning performance benchmark that has become an industry standard, are described, showing growing adoption and improvements to software-stack performance and scalability.

TL;DR: MLPerf Inference as mentioned in this paper is a benchmarking method for evaluating ML inference systems with different architectures and architectures. And it is based on 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.

TL;DR: MLPerf is an ML benchmark standard driven by academia and industry and establishes a standard benchmark suite with proper metrics and benchmarking methodologies to level the playing field for ML system performance measurement of different ML inference hardware, software, and services.