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The AI development lifecycle spans preparing data, training, evaluating, retraining, and inferencing in production. One thing that is common across all lifecycle phases is the need for concurrent IO. Throughout the training/retraining phases, GPUs read data from storage and write checkpoints back out. In large GPU clusters this requires hundreds and sometimes thousands of simultaneous reads and writes to the storage system. To meet this type of demand, the storage platform must be able to handle sustained and transient concurrency peaks with ease.
PowerScale was originally designed and optimized for industries with workloads that require extreme read and write concurrency. This long heritage and deep in-market experience ideally positioned PowerScale to meet the AI infrastructure market’s needs when it emerged.