Trial
Why?
- Training and large-batch inference workflows are IO intensive; NVMe and GPUDirect reduce CPU overhead and latency.
- Efficient data pipelines to GPUs are essential to avoid compute idle time and improve cost-effectiveness.
What?
- Evaluate GPUDirect Storage, NVMe-over-Fabrics, and tiered storage patterns (hot caches on local NVMe).
- Benchmark end-to-end data ingestion paths and integrate with training pipelines and orchestration.
- Align storage topology with dataset lifecycle and backup/replication policies.