Storage Requirements for AI

While GPUs often steal the limelight, it’s essential to recognize the significant role that storage plays in Artificial Intelligence (AI) infrastructure solutions. Throughout the entire AI lifecycle, from data preparation to pre-training, fine-tuning, checkpointing, and inference, storage systems are critical. They not only keep GPUs busy but also safeguard valuable data. In this presentation, we delve into each of these stages, using concrete examples to highlight common patterns and identify key requirements, particularly related to performance. Additionally, we explore the importance of Deep Learning framework data loader libraries and discuss trade-offs between file-based and object-based storage. By attending this talk, participants will gain a better understanding of AI storage workloads and be better equipped to assess their own infrastructure needs.
Presented by John Cardente, Member of Technical Staff, Dell Technologies
at the SNIA Compute, Memory, and Storage Summit
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