The True Cost of AI Inference Is Hiding in the Storage Room

The cost of storing the data generated during 'inference'—the process by which AI produces answers—is emerging as a problem rivaling the cost of training AI itself. MIT Technology Review (October 22, 2026, Tim Rausch) reported that the data left behind by inference is placing a new burden on backend storage infrastructure.

The crux of the problem is that inference never stops. Training can be budgeted and wrapped up like a project with a beginning and an end, but inference leaves data behind every single time a prompt comes in. According to internal measurements by Western Digital (WD), an eight-second AI-generated video is only 3.97MB at 720p resolution—but when you add up all the data left over from the entire inference process, including logging, temporary storage files (caches), rendering, and backups, it exceeds 500MB, roughly 130 times the file the user actually receives.

The scale is only expected to grow. McKinsey projects that demand for AI inference will grow 35% annually through 2030, overtaking training by 22%, and that inference will account for more than 40% of total data center demand before the end of this decade. An IDC white paper sponsored by WD backs this up. In a survey of 763 IT and business decision-makers, about 95% said generative AI had increased their data volumes over the past 12 months, and 61% reported an increase of 25% or more.

A senior product manager at a hyperscale cloud provider pinpointed the heart of the storage problem. "Expanding storage capacity is an optimization for keeping GPUs running without interruption," the manager said, adding that "the biggest challenge is making sure GPUs aren't sitting idle waiting for data." Storage planning, in other words, has already become a core element of AI infrastructure optimization.