AI projects are usually planned around models and GPUs. The network shows up later, often as the reason a training run took twice as long or an AI feature felt slow. These questions help you get ahead of it.
Data movement
- Where does your training data live today? On-premises storage, one cloud, several clouds or a SaaS platform each need a different path.
- How much data moves per training run? Multiply dataset size by the number of runs you expect each quarter.
- How fast must it arrive? Idle GPU time is expensive. Work out the transfer rate that keeps your cluster busy.
Inference traffic
- Where are your users and applications? Inference that sits far from users adds latency to every request.
- Which AI providers will you call? Check which offer private interconnection and in which US regions.
- What is your peak request volume? Size for peaks, not averages.
Security and compliance
- Which data is sensitive? Personal, health or financial data should travel over private paths where possible.
- Who can reach AI endpoints? Tie access to identity and segment AI workloads from the rest of the network.
Cost and operations
- What will data transfer cost? Model egress and interconnection charges across providers before you commit.
- How will you know when something slows down? Baseline throughput and latency now so you can spot changes later.
If several answers are “we are not sure,” that is normal. A short assessment usually fills the gaps. VergRail and VergMind were designed for exactly these questions.