The recent AI explosion is reshaping storage architectures in data centers, where GPU servers increasingly need to access vast amounts of data on network-attached disaggregated storage servers for more scalability and cost-effectiveness. However, conventional CPU-centric servers encounter critical performance and scalability challenges. First, the software mechanisms required to access remote storage over the network consume considerable CPU resources.

Citigroup Inc. analysts quote, "Enterprise data is expected to continue to grow at over 40% CAGR as AI becomes an incremental driver for data creation, storage, and data management."

Today's AI ecosystem require fundamental shifts in the requirements of every datacenter infrastructure component. The predominant AI infrastructure strategy tends to currently focus on the most drastically impactful infrastructure components, as in GPUs, CPUs and Memory.​

The SNIA TC AI Taskforce is working on a paper on AI workloads and the storage requirements for those workloads. This presentation will be an introduction to the key AI workloads with a description of how they use the data transports and storage systems. This is intended to be a foundational level presentation that will give participants a basic working knowledge of the subject.

Join us for an in-depth exploration of how Azure Blob Storage (Azure's object storage service) has innovated and scaled to meet the demands of supercomputer AI training efforts. Using OpenAI as a case study, we will dive into the internal architecture and new capabilities that enable blob storage to deliver tens of terabits per second (Tbps) of consistent throughput across a multi-exabyte (EiB) scale hierarchical namespace. Attendees will gain insights into workload patterns, best practices, and the implementation strategies that make such high performance possible.

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