What is the hot and cold data tiering strategy for large model storage?

The hot and cold data tiering strategy for large model storage refers to classifying data based on access frequency and assigning it to different storage tiers to optimize cost and performance. Hot data, which is accessed frequently, is stored in high-performance, low-latency storage (e.g., SSDs or in-memory caches) to ensure quick retrieval. Cold data, which is rarely accessed, is moved to cost-effective, high-capacity storage (e.g., HDDs or object storage) to reduce expenses.

Explanation:
Large models generate massive datasets, including training logs, model checkpoints, and inference outputs. Not all data is accessed equally—recent checkpoints or frequently used embeddings are "hot," while older versions or infrequently used logs are "cold." Tiering ensures that high-demand data is readily available without paying premium costs for rarely used data.

Example:

• Hot Tier: Current model weights and recent training data stored on NVMe SSDs for fast loading during training or inference.
• Cold Tier: Older model versions or deprecated datasets archived in object storage (e.g., Tencent Cloud COS with Standard-IA or Archive storage classes) to save costs.

For cloud implementations, services like Tencent Cloud COS (for cold data) and Tencent Cloud CFS or Cloud Block Storage (for hot data) can automate tiering based on policies, moving data between tiers dynamically. Tools like lifecycle rules can transition infrequently accessed objects to cheaper storage automatically.