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MLCommons’ MLPerf Storage benchmark addresses the rapidly growing need for high-performance storage in AI training. Driven by the exponential increase in data volume and the even faster growth in data access demands, the benchmark aims to provide a standardized way to compare storage systems’ capabilities for AI workloads. This benefits purchasers seeking informed decisions, researchers developing better storage technologies, and vendors optimizing their products for AI’s unique data access patterns, which are characterized by random reads and massive data volume exceeding the capacity of most on-node storage solutions.
The benchmark currently supports three training workloads (UNET 3D, ResNet-50, and CosmoFlow) using PyTorch and TensorFlow, each imposing distinct demands on storage systems. Future versions will incorporate additional workloads, including a RAG (Retrieval Augmented Generation) pipeline with a vector database, reflecting the evolving needs of large language model training and inference. A key aspect is the focus on maintaining high accelerator utilization (aiming for 95%), making the storage system’s speed crucial for avoiding costly GPU idle time. The benchmark offers both “closed” (apples-to-apples comparisons) and “open” (allowing for vendor-specific optimizations) categories to foster innovation.
MLPerf Storage has seen significant adoption since its initial release, with a substantial increase in the number of submissions and participating organizations. This reflects the growing importance of AI in the market and the need for a standardized benchmark for evaluating storage solutions designed for these unique demands. The benchmark’s community-driven nature and transparency are enabling more informed purchasing decisions, moving beyond arbitrary vendor claims and providing a more objective way to assess the performance of storage systems in the critical context of modern AI applications.
Personnel: Curtis Anderson, David Kanter
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