AI changes the storage question
In AI infrastructure, the bottleneck may sit anywhere in the path before data reaches compute: dataset reads, decompression, caching, networking, file-system scheduling or checkpoint writes. A peak sequential number is therefore not a direct measure of training throughput or inference response time.
NVMe was designed for non-volatile storage and supports parallel command processing through multiple queues. The NVM Express organization identifies lower latency, higher IOPS and data-center scalability as key benefits.
Model four data paths
| Path | What to validate |
|---|---|
| Dataset preparation | Concurrent reads, file system, CPU decompression and cache behavior |
| Checkpoints | Sustained writes, endurance, power-loss handling and recovery |
| Inference and retrieval | Tail latency, QoS, capacity planning and cache-hit ratio |
| Logs and operations | Endurance, isolation, retention and monitoring |
Practical evaluation
Use production-like servers, operating systems, drivers, data sets and I/O patterns. Record stable-state behavior, capacity utilization, spare-area policy, thermal conditions and firmware versions. The useful target is reproducible throughput and latency for a defined workload, not a single headline benchmark.