AI’s hidden storage bill: What inference leaves behind
The article notes that as inference scales, this hidden storage footprint can grow into hundreds of gigabytes per clip for higher‑resolution content.
Key points
- McKinsey forecasts AI inference demand to grow 35% CAGR through 2030, outpacing training and reaching >40% of data‑center demand.
- IDC survey of 763 IT and business leaders found 95% see AI data volume rise, 61% report ≥25% growth, and 96% need faster archive retrieval.
McKinsey projects AI inference demand to grow at a 35% compound annual growth rate through 2030, compared with 22% for training, and to account for more than 40% of total data‑center demand by the decade’s end. A WD‑sponsored IDC white paper surveying 763 IT and business decision makers found nearly 95% reporting an increase in data volume due to generative AI in the past year, about 61% seeing growth of 25% or more, and 96% anticipating a need for faster archive retrieval. A senior product manager of a hyperscale cloud service provider warned that storage bottlenecks can cause GPU idle time, inflating costs.
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
More in Enterprise & Industry
All →- Celonis chief scientist says enterprise AI needs operational context · 1 src
- Tacticaai partners with NVIDIA to integrate Nemotron models for agentic AI · 1 src
- Cyware launches Last Mile AI agents for threat prioritization · 1 src
- Polarise launches sovereign AI Cloud with Rise chatbot and Drive inference platform · 1 src
- Palantir reports 95% cost reduction for L3Harris and 30,000‑hour savings for McCarthy · 1 src
Comments
via GitHub Discussions