MLCommons presented MLPerf Client, a new benchmark designed to measure the performance of PC-class systems, including laptops and desktops, on large language model (LLM) tasks. Released in December 2024, it's an installable, open-source application (available on GitHub) that allows users to easily test their systems and provides early access for feedback and improvement. The initial release focuses on a single large language model, LLaMA 2.7 billion, using the Open Orca dataset, and includes four tests simulating different LLM usage scenarios like content generation and summarization. The benchmark prioritizes response latency as its primary metric, mirroring real-world user experience.

A key aspect of MLPerf Client is its emphasis on accuracy. While prioritizing performance, it incorporates the MMLU (Massive Multitask Language Understanding) benchmark to ensure the measured performance is achieved with acceptable accuracy. This prevents optimizations that might drastically improve speed but severely compromise the quality of the LLM's output. The presenters emphasized that this is not intended to evaluate production-ready LLMs, but rather to provide a standardized and impartial way to compare the performance of different hardware and software configurations on common LLM tasks.

The benchmark utilizes a single-stream approach, feeding queries one at a time, and supports multiple GPU acceleration paths via ONNX Runtime and Intel OpenVINO. The presenters highlighted the flexibility of allowing hardware vendors to optimize the model (LLaMA 2.7B) for their specific devices, even down to 4-bit integer quantization, while maintaining sufficient accuracy as judged by the MMLU threshold. Future plans include expanding hardware support, adding more tests and models, and implementing a graphical user interface (GUI) to improve usability.

Presented by David Kanter, Executive Director, MLCommons live in San Jose, California on January 29, 2025 as part of AI Field Day 6. Watch the entire presentation at https://techfieldday.com/appearance/ml-commons-presents-at-ai-field-day-6/ or visit https://TechFieldDay.com/event/aifd6/ or https://MLCommons.org for more information.