During a months-long training run of a foundation model, the system periodically saves large model checkpoint files. These files are massive, written sequentially, read only during crash recovery, and do not need a hierarchical directory structure. Which storage type is the most cost-effective and appropriate for archiving these checkpoint files?
Select an answer to reveal the explanation.
Short Explanation
Think of checkpointing like saving your game progress in a massive RPG. Every few hours, you save the entire model state—often hundreds of gigabytes—so a burned-out GPU or power cut doesn't cost you weeks of training. Since those files are huge, written sequentially, and read almost never, object storage is the cheap, scalable place to park them instead of paying for hot file or block storage.
Full Explanation
Model checkpoints are copies of the complete state of a model (including weights, biases, optimizer states, and metadata) saved at specific intervals during training. This ensures that if a hardware failure occurs in a multi-node cluster, training can resume from the last saved state rather than restarting from scratch. These checkpoint files have distinct storage characteristics: large file sizes, write-once/read-rarely patterns, and flat structure (no POSIX directory requirements). Object storage (such as AWS S3, Google Cloud Storage, Azure Blob Storage, or on-premises Ceph) is the most cost-effective solution for this workload. It offers highly durable, virtually unlimited scale-out capacity at a fraction of the cost of block or file storage. It uses a flat namespace and simple API-driven access (PUT/GET), which perfectly aligns with asynchronous checkpointing. High-performance Parallel File Systems like Lustre or GPFS are extremely fast but highly expensive, and they are best reserved for the active, hot training loop where active data loading requires high-throughput, low-latency random reads. Distributed Block Storage with NVMe over Fabrics is designed for low-latency database or OS workloads and is cost-prohibitive for archiving terabytes of sequential files. Network File System shares suffer from scaling limitations and performance bottlenecks when multiple nodes attempt to write massive files simultaneously over a shared mount point.