NFS is still the practical data path for AI training
Why ZetaNAS treats NFS as a first-class training data interface instead of pretending every AI workload needs a new storage abstraction.
AI training usually wants predictable data access and exclusive GPU scheduling. The storage layer should not pretend that sharing files is the same thing as sharing GPUs.
ZetaNAS keeps NFS ready because many teams need a simple, boring, reliable way to mount datasets across training nodes, preprocessing jobs, annotation tools, and review workflows. The GPU can stay exclusive to the job that owns it, while data remains reachable through a familiar interface.
For small and medium-sized teams, that matters more than fashionable complexity:
- NFS is easy to operate and debug.
- Existing Linux tooling understands it.
- Training jobs can mount shared datasets without moving every file into object storage first.
- Teams can pair it with S3/POSIX access where application pipelines need different interfaces.
The goal is not to make storage sound exotic. The goal is to keep data close enough, consistent enough, and visible enough that expensive compute does not wait on confused infrastructure.