What is AI storage?

This episode of Techsplainers explores AI storage and shows how storage infrastructure changes when it is designed around modern AI workloads. Building on earlier discussions of data storage, storage architectures, flash, intelligent storage and edge storage, the episode shifts the focus to the demands created by model training, inference and large-scale data pipelines. The episode explains why AI workloads need more than raw capacity. It examines the need for high throughput, low latency and scalable infrastructure that can support accelerators, large datasets and constant data movement. It also looks at how AI environments rely on file, object and block storage in different ways, depending on the workload and access pattern. Listeners will also hear how data tiering, versioning, metadata, security and governance all shape effective AI storage strategies. Real-world examples from healthcare and retail help connect these ideas to practical use cases. The result is a clear overview of why AI storage is not a separate world from traditional storage, but a specialized approach built on the same foundations. Learn more in https://www.ibm.com/think/topics/ai-storage Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Elly Trickett "AI tools may be used to transcribe this episode and support selected stages of the production process. All AI-assisted content is reviewed by the production team before publication."