Storage Architecture Advanced

AI workloads have diverse storage requirements: fast model loading, high-throughput data pipelines, checkpoint storage for training, and cost-effective archival for datasets. A well-designed storage architecture addresses all of these needs.

Storage Tiers

• Tier 1 - NVMe Local: Fastest storage for model loading and active checkpoints. 3-7GB/s per drive.
• Tier 2 - Parallel File System: Lustre or GPFS for shared datasets and model weights across the cluster.
• Tier 3 - Object Storage: MinIO or Ceph for model registry, training data archives, and backup.

Data Pipeline Storage

• Training data pipelines need sustained sequential read throughput. Plan for at least 1GB/s per GPU for data loading.
• Use data loaders with prefetching and caching to overlap I/O with compute and avoid GPU idle time.

Checkpoint Management

• Large model training checkpoints can be 100GB+ each. Plan for frequent checkpointing (every 15-30 minutes) with automated cleanup of old checkpoints.
• Implement a checkpoint lifecycle policy: hot storage for recent checkpoints, warm storage for the last week, cold storage for archival.

Backup & Recovery

• Back up model weights, training configurations, and evaluation datasets. These are your most valuable data assets.
• Implement disaster recovery with off-site replication for critical models and datasets.