Use Case · AI/HPC Cold Tier
Retain training datasets, simulation output, and reproducibility archives without keeping all data on always-on storage.
AI and HPC programs generate datasets that must be retained for reproducibility and governance, but only a subset remains active.
Tape libraries support long-horizon cold data retention while reducing dependence on always-on storage for infrequently accessed datasets.
Architecture steps
- Keep active datasets on warm performance tiers for immediate compute access.
- Move completed runs and reproducibility datasets into automated tape retention policies.
- Rehydrate selectively for model validation, retraining, or compliance review.
Governance controls
- Tag retained datasets with policy metadata for reproducibility and audit trails.
- Define retrieval SLA classes by project criticality.
- Use standardized formats and LTFS-oriented workflows for portable recovery.
Best-fit Qualstar platforms
Industries where this is common
Cloud data centers
Supports high-growth cold tiers for multi-tenant infrastructure.
Information technology
Helps IT teams balance performance and long-term retention costs.
Education
Fits research and university compute archives with long retention horizons.