More than RAM size and SSD capacity
AI and data-intensive systems move data among RAM, NVMe, network file systems and object storage. Insufficient memory can force active data to storage; insufficient local SSD capacity or endurance can crowd checkpoints, cache or temporary data onto critical paths.
Four planning variables
| Variable | Planning question |
|---|---|
| Working set | How much data must remain active for a job? |
| Concurrency | How many jobs, containers or users share the node? |
| Checkpoint and recovery | How large and frequent are writes, and what recovery time is allowed? |
| Data location | Where do hot, cold and shared data reside? |
DDR5 RDIMMs are designed for server, workstation and database environments, but actual capacity and speed must follow the server and CPU support matrix. For NVMe, include OS, images, cache, temporary files, checkpoints, spare area and recovery buffer; align endurance with observed host writes.
Execution order
- Collect working-set, concurrency, write and recovery objectives.
- Classify nodes by their dominant constraint.
- Design memory channels and NVMe capacity/endurance around actual data paths.
- Validate pressure and recovery on representative nodes, then operationalize thresholds and expansion rules.