Fast Facts
- Inference never stops—and neither does its data. An 8-second AI video can generate over 500MB of backend data, about 130× the delivered file size.
- AI data is growing fast. Nearly 95% of surveyed organizations reported more data from generative AI in the past year; about 61% saw growth of 25% or more.
- Storage is becoming a performance issue. If GPUs wait for data, costly compute sits idle—and retained AI data keeps accumulating even when compute demand dips.
- Plan storage around the data lifecycle. AI needs both fast access and affordable capacity; HDDs offer a cost-effective foundation for large datasets that must be retained and reused.
Every answer creates a data trail
AI training runs end. Inference keeps going, serving each prompt and generating data along the way. That trail can include logs, cached context, temporary renders, backups and safety records.
Consider an eight-second AI video. WD’s internal measurements found that a 720p clip, delivered as a 3.97-megabyte file, created more than 500 megabytes of associated back-end data — about 130 times the visible file size. Longer, higher-resolution enterprise videos can leave much larger footprints.
The trend extends beyond video. McKinsey projects inference demand will grow 35% annually through 2030, outpacing training at 22%. A WD-sponsored IDC survey of 763 decision-makers found that nearly 95% saw generative AI increase data volumes over the past year. About 61% reported growth of at least 25%.
Storage demand keeps rising
Compute budgets often follow training cycles and product launches. Retained data does not shrink when those cycles slow. Logs, generated content and inference results accumulate, and teams may keep them for future use.
That storage can help keep costly GPUs busy by making data available when workloads need it. But it also adds a bill that may grow faster than expected. More retention can support analysis and future AI applications; keeping everything, however, can raise costs without adding value.
Match storage to the data’s next job
Some AI data needs fast access. Other data needs affordable, high-capacity storage until a team needs it again. The right mix depends on how often teams retrieve information and how quickly they need it.
A WD-IDC survey found that 96% of respondents expect to need faster archive retrieval for inference or retrieval-augmented generation. Hard disk drives can provide a cost-effective foundation for large volumes of retained data, while faster storage serves more time-sensitive work. As adoption grows, teams can plan for both performance and capacity — and set clear rules for what they keep.
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