Lilly Tech Systems
Introduction to AI Data Storage Beginner
Storage is often the overlooked bottleneck in AI infrastructure. While teams focus on GPU compute, slow data loading can leave expensive GPUs idle 30-50% of the time. This lesson introduces the unique storage challenges of AI workloads and the architecture patterns that keep GPUs fed with data at full speed.
AI Storage Challenges
- Scale — Training datasets can be petabytes; ImageNet is 150GB, but production datasets are often 10-100TB+
- Throughput — A single GPU can consume 1-10 GB/s of training data; 8 GPUs need 8-80 GB/s aggregate
- Random access — Training data loaders shuffle data, creating random I/O patterns that are hostile to traditional storage
- Checkpointing — Large model checkpoints (10-100GB) must be written periodically without stalling training
- Shared access — Multiple training jobs and data pipelines access the same datasets simultaneously
Data Access Patterns
| Workload | Pattern | Storage Need |
|---|---|---|
| Training data loading | Sequential read, shuffled | High throughput, random IOPS |
| Checkpointing | Large sequential write | High write bandwidth |
| Model serving | Read once, hold in memory | Fast initial load, low ongoing I/O |
| Feature store | Random read, low latency | Low-latency key-value access |
| Data pipeline | Large sequential read/write | High throughput bulk processing |
Storage Architecture Overview
A well-designed AI storage architecture uses multiple layers:
- Local NVMe (hot cache)
Fastest storage on each GPU node; used for active training datasets and checkpoints.
- Shared parallel filesystem (warm)
Lustre, GPFS, or NFS serving datasets to all nodes; the primary data access layer.
- Object storage (cold/archive)
S3, GCS, or MinIO for long-term storage of datasets, models, and experiment artifacts.
Key Insight: The cost of GPU idle time due to data starvation almost always exceeds the cost of faster storage. If your GPUs are idle 20% of the time waiting for data, you are effectively wasting 20% of your GPU investment. Better storage pays for itself.
Ready to Learn Storage Tiers?
The next lesson covers designing multi-tier storage architectures for AI workloads.
Next: Storage Tiers →