Download the full report.
In this new whitepaper from our friends over at Panasas, we take a look at whether your storage infrastructure is ready for the robust requirements in support of AI workloads. AI promises to not only create entirely new industries, but it will also fundamentally change the way organizations large and small conduct business. IT planners need to start revising their storage infrastructure now to prepare the organization for the coming AI wave.
This guide includes 4 important chapters that focus on the new levels of storage infrastructure needed for the demands of AI:
Chapter 1 – Is Your Storage Architecture Ready for the Coming AI Wave?
Chapter 2 – Understanding the Challenges that AI at Scale Creates
Chapter 3 – Can Current Storage Infrastructure Meet the AI at Scale Demand?
Chapter 4 – The Requirements of AI at Scales Storage Infrastructures
AI/ML workloads are fundamentally different from any other workload the organization may have run in the past. Early AI/ML projects have counted on DAS for data storage. The problem is that DAS doesn’t distribute the load evenly, something that is critical as the number of GPUs per AI workload increases. Also, DAS is highly inefficient, and the waste in capacity and time spent copying or moving data eliminates the price advantage of cheap internal drives.
Panasas data storage provides the extreme performance, enterprise-grade reliability and manageability required to process the large and complex datasets associated with mixed workload HPC environments as well as emerging applications like AI, AR, VR, precision medicine, and autonomous driving.
Download the new whitepaper courtesy of Panasas, “Is Your Storage Infrastructure Ready for the Coming AI Wave?” to understand how IT planners need to start revising their storage infrastructure now to prepare the organization for the coming AI wave.
