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Accelerate Time to Value and AI Insights

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For artificial intelligence (AI) and deep learning (DL) workloads, data sets can range from hundreds of terabytes to tens of petabytes—and processing that data can easily result in I/O bottlenecks if storage can’t flexibly scale out as needed.

Moreover, these neural network models must be able to fully utilize GPU resources without saturating storage resources; but how?

Read this technical paper to learn how software-defined storage fulfills the I/O performance demands of AI and DL workloads and reduces the overall AI development cycle.

Vendor:
HPE and Intel®
Posted:
29 Aug 2019
Published:
31 Dec 2018
Format:
PDF
Length:
14 Page(s)
Type:
White Paper
Language:
English

This resource is no longer available.