The business world is being transformed rapidly by artificial intelligence (AI), high-performance computing (HPC), analytics, and hybrid cloud technologies. Unlike traditional applications that work with structured data that is stored in databases, these new workloads operate on a vast ocean of unstructured data, such as documents, images, videos, audio recordings, log files, or any other information that can be generated by users, applications, or even machines.
Therefore, organizations are having to rethink their data storage strategies and adapt to new ways of working, including how to use AI to unlock the value of their data wherever it might reside. IT leaders face many new challenges.

Accessing and analyzing data that is scattered across the globe
Increasing time and resources that are needed by AI model training and inferencing workloads
The cost and scarcity of resources, such as NVIDIA graphics processing units (GPUs).

Addressing these challenges requires specialized software and hardware.

IBM Storage Scale. Software-defined file and object storage for both structured and unstructured data.
IBM Storage Scale System 3500. A hardware implementation of IBM Storage Scale software that is designed for organizations requiring enterprise-ready, entry-level, or mid-level storage system.
IBM Storage Scale System 6000. A hardware implementation of IBM Storage Scale software that is optimized for the most demanding AI, HPC, analytics, and hybrid cloud workloads.

This document explores the latest IBM Storage Scale System hardware, which addresses these new and growing needs through many different system and storage configurations. This document is intended to be used by technical support staff, IT architects, IT specialists, and anyone who wants to know more about the latest IBM Storage Scale System offerings and system components.

Mika Heino