How does cloud collaborative storage reduce the cost of large model training?

Cloud collaborative storage reduces the cost of large model training through several key mechanisms, primarily by optimizing resource utilization, enabling efficient data sharing, and minimizing redundant infrastructure. Here’s a breakdown with examples:

1. Shared Storage Pool: Collaborative storage allows multiple teams or nodes to access a centralized, scalable storage system. Instead of each training instance maintaining separate copies of datasets or model checkpoints, the data is stored once and accessed dynamically. This reduces storage overhead. For example, a team training a large language model can store the dataset (e.g., terabytes of text) in a shared cloud storage bucket, accessible to all GPUs/TPUs, eliminating the need for local duplication.

2. Elastic Scalability: Cloud storage services can scale storage capacity up or down based on demand. During peak training phases, additional storage can be provisioned temporarily, and unused resources are released afterward, avoiding over-provisioning costs. For instance, if a model training job requires 100 TB of temporary checkpoint storage, the cloud platform can allocate it only when needed and decommission it post-training.

3. Data Versioning & Checkpoint Management: Collaborative storage often includes built-in version control and checkpointing features. This ensures that only the most relevant versions of models and datasets are retained, reducing waste. For example, a research team can store iterative model checkpoints (e.g., every 10 training epochs) and retrieve older versions if needed, without manually managing backups.

4. Cost-Efficient Redundancy: Cloud providers offer tiered storage options (e.g., hot, cool, archive) to balance performance and cost. Frequently accessed training data can reside in high-performance storage, while less critical data (e.g., historical logs) is moved to cheaper tiers. A large-scale vision model training pipeline might use high-speed SSDs for active datasets and move completed experiment logs to low-cost archival storage.

5. Reduced On-Premises Hardware: By leveraging cloud collaborative storage, organizations avoid investing in expensive on-premises NAS/SAN systems. Instead, they pay for storage as a service, aligning costs with actual usage. For example, a startup training generative AI models can avoid purchasing multi-petabyte storage arrays and instead use a cloud-based distributed file system (e.g., Tencent Cloud’s CFS or COS) for seamless scalability.

Example Workflow:

• A distributed team trains a recommendation model across multiple GPU instances.
• The training dataset (e.g., user interaction logs) is stored in a cloud object storage (like Tencent Cloud COS), accessible to all nodes.
• Model checkpoints are saved to a high-throughput file system (e.g., Tencent Cloud CFS) for quick recovery.
• Unused data is automatically moved to archive storage to cut costs.

By consolidating storage and leveraging cloud elasticity, collaborative storage minimizes capital expenditure (CapEx) and operational expenditure (OpEx) while accelerating training workflows.