This post was originally published on Data Center Knowledge
Ask most technology leaders how to build performant, cost-effective AI applications, and they’ll talk at length about LLMs, data sets, and specialized chips. These are vital, for sure, but they overlook an unglamorous but critical part of the stack that’s key to maximizing the performance and ROI of AI systems: storage.
AI systems consume and produce massive volumes of data, and a poorly designed storage architecture can add significant costs. According to a white paper from Meta and Stanford University, storage can consume as much as one-third of the power required to train deep learning models. For CIOs and engineering leaders planning AI deployments, understanding the role storage plays and how to optimize it is essential to ensuring project success.
AI accelerators – and GPUs in particular – are among the most expensive and scarce resources in modern data centers. When a GPU sits idle waiting for data, your organization is essentially burning money. An incorrect storage configuration can greatly reduce effective GPU throughput, transforming high-performance computing into an expensive waiting game.
How Storage Bottlenecks Sabotage AI Chip Performance
The fundamental issue is that GPUs and TPUs (Tensor Processing Units) can process data far faster
— Read the rest of this post, which was originally published on Data Center Knowledge.
