AI SSD
Storage for AI Inference Serving
Inference storage is often about predictable model loading, cache behavior, and safe rollout rather than raw peak throughput.
Model artifacts
Inference systems need reliable access to model artifacts, tokenizers, configuration, and supporting files. Storage layout should make rollout and rollback predictable.
Cold starts and cache
If models are large, cold starts can become a user-visible delay. Local SSD or NVMe cache may reduce startup time, but cache invalidation and version control must be planned.
Rollout safety
Storage design should support atomic updates, old-version retention, and rollback. A fast device does not solve version confusion or missing artifact problems.
Practical checklist
- Measure model load time from each storage tier.
- Keep versioned model artifacts and rollback paths.
- Separate cache from durable source of truth.
- Plan capacity for multiple active model versions.
Related storage topics
- NVMe storage for low-latency flash planning.
- HDD storage for capacity and archival planning.
- AI SSD storage for dataset, checkpoint, and inference workflows.
- Storage Resources for additional planning articles.