Energy Benchmarking for AI Storage
San Tomas + Lawrence
Tue Sep 29 | 5:00pm
Abstract
Energy availability, consumption, and associated externalities are a primary limiting factor for datacentre construction and operation. To improve visibility into the energy characteristics of AI - Training, inference and storage workloads, the MLCommons Storage Workgroup has developed a benchmarking methodology and an open source data analysis toolchain built on top of DMTF RedFish that characterizes different workloads tested in the MLPerf benchmarks. Benchmark results provide critical real-world insight to improve data centre power infrastructure design efficiency, reduce utility demand charges, and enable "what-if" planning when adopting new accelerators, storage and models.
This presentation will provide an overview of the MLCommons Power benchmarks, and will demonstrate how correlated load/power benchmarking can reveal important relationships between infrastructure utilization, energy efficiency, environmental impacts and data centre capex costs.
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