Describe the workload before sizing infrastructure
Separate training, fine-tuning, batch inference and real-time inference. For each, record expected demand, latency tolerance, model size, data movement, availability target, budget boundary and how success will be measured. A GPU count alone does not describe the system users will experience.
Write down the path from input to response: identity, application, model endpoint, retrieval data, storage, network, monitoring and human escalation. This reveals shared dependencies and makes the recovery design testable.
Classify the data path
Identify which data enters the AI workflow, whether it includes personal or sensitive data, where it is stored, who can administer it and how long it is retained. Include prompts, retrieved documents, logs, evaluations, backups and vendor-support access.
The Saudi PDPL guidance explains the scope and concepts of personal-data processing. Use it to frame questions, then obtain current legal and compliance advice for the exact organization and arrangement.
Make physical capacity claims measurable
For an on-premises or colocation design, request rack-level power, cooling, cable paths, network handoffs, maintenance constraints and expansion lead times. For a cloud design, document quota, capacity reservation, regional availability, dependencies and recovery boundary.
Measure utilization and bottlenecks over time. A system may be limited by storage throughput, network fabric, model serving concurrency, power delivery or data preparation rather than accelerator availability.
Assign governance and accountability
Name a service owner, data owner, security owner and incident decision-maker. Define approval gates for new models, data sources, deployment changes, access privileges and high-risk use cases. Governance should create records that can be reviewed after an incident.
SDAIA publishes AI-related guidance and an AI Adoption Framework. These resources can inform responsible planning, while each organization must establish controls and approvals appropriate to its own use case.
Demand operating evidence, not demonstrations
Before scaling, request a degraded-service runbook, monitoring example, access-review process, recovery-test plan and a description of how model or data changes are tracked. Demonstrations show a happy path; operating evidence shows whether a service can be managed through change.
Use calculators and scorecards as educational aids only. They make questions and measurement boundaries visible; they do not certify a facility, an AI platform or compliance.
FAQ
Is more accelerator capacity always the answer?
No. The limiting factor may be power, cooling, network fabric, storage, data preparation, quota or operating ownership.