Why does checkpointing affect storage planning?
Why A is the answer
Large checkpoints need fast writes
Slow writes pause training.
In detail
- Large checkpoints need fast writes, because slow writes pause training.
- Plan for write bursts.
Use case
An illustrative scenario, not a real incident.
Writing a large checkpoint pauses training, so slow storage wastes GPU time.
See it in different ways
flowchart TD A["AI storage"] --> B["High read throughput for training"] A --> C["Fast checkpoint writes"] A --> D["Many small files for metadata"]
mindmap
root((AI storage))
Reads
High throughput
Parallel file system
Writes
Fast checkpoints
Direct path
GPUDirect Storagemindmap
root(("Training storage"))
n1["Reads"]
n2["Datasets"]
n3["Writes"]
n4["Checkpoints"]Key point: Checkpoints need fast writes.
Why the other options are wrong
- B. Checkpoints replace the need for reads
- You still read data.
- C. Checkpoints are stored in the BMC
- A BMC holds no checkpoints.
- D. Checkpoints never use any storage
- They use a lot of storage.