Storage Architecture Optimized for AI Workloads

webinar

Author(s)/Presenter(s):

Paul McLeod

Supermicro

Library Content Type

Presentation

Library Release Date

Focus Areas

Networked Storage

Abstract

Storage optimized for AI workloads must have high performance throughput to stage data on GPU cluster servers while also having a very large, cost-effective, capacity optimized mass storage tier to collect, process and label large data sets needed for the AI model training. In this session, Supermicro will discuss AI storage solutions using high performance flash-based storage servers and high-capacity disk storage servers with file and object storage solutions from partners such as WEKA, OSNexus, Quantum ActiveScale and Scality. We will also describe how a 25PB AI-optimized storage implementation was deployed at a leading technology manufacturing company for use in machine vision applications and how similar storage can be deployed for other applications.