AI Storage Best Practices Advanced

This final lesson covers the operational best practices for running production AI storage infrastructure, including capacity planning, performance tuning, disaster recovery, and cost optimization strategies that keep your storage reliable and cost-effective as your AI platform grows.

Capacity Planning

• Track growth rate — Monitor storage consumption trends; AI data typically grows 2-5x per year
• Plan for headroom — Maintain 20-30% free capacity for checkpointing spikes and unexpected data growth
• Separate throughput and capacity planning — You may run out of IOPS before running out of space, or vice versa
• Budget for tiering — Plan storage procurement across tiers; most growth should go to cold storage

Performance Tuning

Disaster Recovery

• Training data — Replicate to a second site or cloud region; training data is irreplaceable if lost
• Model artifacts — Store in object storage with cross-region replication; these are the output of expensive training
• Checkpoints — Replicate critical checkpoints off-node; local NVMe failure should not lose training progress
• Configuration — Store all storage configuration in Git (GitOps); enables rapid recreation of storage infrastructure

Cost Optimization

• Implement lifecycle policies — Automatically tier data to cheaper storage based on access patterns
• Compress cold data — Compress datasets in cold storage; decompress on-the-fly during staging
• Deduplicate — Multiple teams often copy the same datasets; use shared mounts or symlinks instead
• Monitor waste — Track orphaned data, duplicate datasets, and forgotten checkpoints
• Right-size Lustre — Do not over-provision OSTs; scale storage based on actual throughput needs