Checkpointing, Snapshotting, and Recovery AI systems fail in ordinary ways: a node dies, a process is killed, a deployment rolls back, a storage endpoint times out, a batch job is preempted, a human makes a wrong change. What makes AI different is not that failures happen, but that the work is large, stateful, and expensive. […]

IO Bottlenecks and Throughput Engineering Compute gets the headlines, but most large AI systems are limited by the movement of data. The reason is simple: the math scales faster than the plumbing. A modern accelerator can consume tensors at a pace that turns small inefficiencies into large costs. When the input pipeline falls behind, the […]

Storage Pipelines for Large Datasets A modern AI stack can burn through GPU time at a rate that makes storage look slow, even when storage is “fast” by traditional standards. This is why storage pipelines matter. If data cannot reach the GPUs in the right shape and at the right rate, the cluster becomes a […]