Map the lifecycle
Separate raw data, cleaned outputs, samples, checkpoints and archives, defining capacity and retention.
AI / REFERENCE ARCHITECTURE
AI Data Pipelines & Storage Tiers
Connect ingestion, preparation, training and backups so data keeps pace with compute.
Hardware and application reference imageryBUILD AROUND THE WORKLOAD
Work through the technical requirements
Define storage tiers
Use local NVMe where appropriate for hot data; assess NAS, file or object storage for sharing rather than one medium for every role.
Check the data path
Profile loaders, CPU decoding, networks and storage. Sequential benchmarks do not represent small-file workloads.
Recovery & governance
Establish versions, permissions and backups; define acceptable data loss and recovery time and validate with restoration drills.
REFERENCE WORKFLOW
Define the workflow before the hardware.
- 01Workload
- 02Compute
- 03Data & links
- 04Validate & operate
This is an architecture planning approach, not a completed case study or performance commitment. Agree software, equipment and service scope per project.
What do we need next?
Applications and models, data scale, user or node counts, latency and throughput goals, existing resources, budget and site constraints. Existing measurements help narrow the shortlist.
Turn requirements into a reviewable plan
Discuss selection, bills of materials, samples and procurement on a project basis. Model availability, implementation, installation and validation scope require agreement after requirements review.
Discuss your technical requirements ↗LET’S BUILD YOUR NEXT CONFIGURATION
Discuss your requirements ↗