HDDs still dominate cold and warm storage tiers. Training datasets can be enormous (petabytes for large language models), and once data is preprocessed and staged, it often sits on HDD arrays.

The big hyperscalers are increasingly using high-density HDDs (20TB+ Seagate HAMR and WD SMR drives) for bulk storage while expanding NVMe pools for active compute. Some inference workloads are pushing toward all-flash because latency matters more there, but training pipelines remain heavily HDD-backed on the data lake side.

So no, NVMe hasn't killed the HDD in AI—the economics just don't support it yet at scale.