When a regional healthcare provider in Southeast Asia decided to bring AI in-house for patient data analysis, they faced a familiar challenge: how to design infrastructure for machine learning workloads without the resources of a major cloud provider. Their starting point was modest—a 10-rack computer room that had served general IT needs for years.
AI on a Budget
The team wanted to run open source models (Llama 2 and Mistral) locally to maintain data sovereignty over patient records. Their constraints were realistic:
Traditional AI infrastructure guides assumed unlimited budgets and dedicated facilities. They needed something different.
The Design Approach
Using digital twin modeling, the team validated several critical elements before procurement:
Power Density Planning
Rather than assuming maximum density everywhere, they modeled actual GPU power draws with realistic redundancy scenarios. This prevented over-provisioning power infrastructure while ensuring adequate capacity.
Cooling Validation
The existing cooling system, adequate for general compute, required validation against GPU thermal loads. Modeling identified specific rack positions that could handle the heat without facility-wide HVAC upgrades.
Network Topology
East-west traffic between GPUs demanded careful fiber planning. The design validated latency budgets and identified where standard fiber paths would create bottlenecks.
Documentation Strategy
With a small team, knowledge transfer was critical. The digital twin became the single source of truth—accessible to any team member, not locked in one engineer's expertise.
Results
You don't need hyperscale resources to run sophisticated AI. You need design validation that matches your scale—tools that help small teams make smart decisions before committing hardware budgets.
See how Routemaster's digital twin platform helps regional operators deploy AI infrastructure efficiently.