AI Storage Tiering: When to Use HDDs and SSDs
AI infrastructure should not treat every dataset as if it has the same performance requirement. Storage tiering assigns data to media based on latency, throughput, access frequency, capacity, and retention. HDDs and SSDs serve different roles, and many AI environments use both.
Use SSDs for Latency-Sensitive Processing
SSDs are suited to active training datasets, high-frequency inference pipelines, metadata operations and workloads that require low latency or high random I/O performance. The exact requirement depends on the model, accelerator architecture, and data pipeline.
Use HDDs for Capacity-Oriented AI Data
Enterprise HDDs are suited to data lakes, object storage, retained source data, checkpoints, backups, logs, and archives. These datasets can remain accessible without occupying the highest-cost storage tier.
Build Data Movement Policies
Storage teams should define when data enters a performance tier, how long it remains there and when it returns to a capacity tier. Automated lifecycle policies can reduce manual movement and prevent premium storage from filling with inactive data.
Account for Checkpoints and Model Versions
Model checkpoints and versioned datasets can consume substantial capacity. Recent checkpoints may require fast access, while older versions may be moved to a high-capacity tier for rollback, audit or reproducibility.
Evaluate TCO Across the Full Architecture
A tiered design should compare acquisition cost, power, cooling, rack space, network requirements, software and operational complexity. The lowest-cost component does not automatically produce the lowest system TCO.
Product Selection
Toshiba Enterprise HDDs support cloud-scale capacity, enterprise capacity, and performance-oriented storage categories. Cloud-scale MG Series HDDs are designed for high-capacity 24/7 infrastructure, including file, object, backup, and tiered storage.
Frequently Asked Questions
Are HDDs or SSDs better for AI?
Neither is universally better. SSDs support latency-sensitive processing, while HDDs provide high-capacity storage for data lakes, backups, and retained datasets.
What data should be placed on an AI performance tier?
Active training data, frequently accessed model artifacts and latency-sensitive inference data are common candidates.
What data should be placed on an AI capacity tier?
Raw datasets, older checkpoints, backups, logs, generated outputs and archive content are common candidates.
Can data move automatically between HDD and SSD tiers?
Yes. Storage software can apply lifecycle policies based on age, access frequency, capacity, and application requirements.
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