NEWS · MONEY · #2399
WD/IDC white paper: AI inference is inflating storage needs and costs
A WD‑sponsored IDC white paper and WD internal measurements highlight that AI inference generates large persistent back-end data that dramatically increases storage footprints and operating costs—an 8‑second, 720p (3.97MB) clip produced roughly 500MB of associated backend data (≈130× the delivered file). McKinsey projects inference demand will grow at a 35% CAGR through 2030, IDC survey respondents (n=763) largely reported rising data volumes (≈95% saw increases; ≈61% saw ≥25% growth) and 96% expect faster archive retrieval will be needed for inference/RAG workflows.
KEY POINTS
- A WD‑sponsored IDC white paper and WD internal measurements highlight that AI inference generates large persistent back-end data that dramatically increases storage footprints and operating costs—an 8‑second, 720p (3.97MB) clip produced roughly 500MB of associated backend data (≈130× the delivered file).
- McKinsey projects inference demand will grow at a 35% CAGR through 2030, IDC survey respondents (n=763) largely reported rising data volumes (≈95% saw increases; ≈61% saw ≥25% growth) and 96% expect faster archive retrieval will be needed for inference/RAG workflows.
- Because inference produces persistent, reusable backend data at scale, storage — not just GPUs — can become a material and growing line item that requires different architecture and budgeting.
WHY IT MATTERS
Because inference produces persistent, reusable backend data at scale, storage — not just GPUs — can become a material and growing line item that requires different architecture and budgeting.