AI SSD
AI Training Checkpoint Storage Considerations
Checkpoint storage can create heavy write bursts and retention pressure during AI training workflows.
Why checkpoints matter
Training checkpoints protect progress and make experiments reproducible. They can also create large, repeated writes that stress local disks, network storage, and cleanup policies.
Write bursts and retention
A useful checkpoint plan defines how often checkpoints are written, how many are retained, and when they move to colder storage. Without retention rules, checkpoint directories can consume capacity quickly.
Recovery and locality
The best checkpoint location balances write speed, recovery time, and durability. Local NVMe may help active training, while object storage or HDD-backed capacity may fit longer retention.
Practical checklist
- Estimate checkpoint size and frequency before training starts.
- Define retention and cleanup rules.
- Keep active checkpoints close to compute where needed.
- Move older checkpoints to a lower-cost durable tier.
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.