Running effective local AI is not simply a matter of installing software on a powerful computer. It requires deliberate hardware choices, proper system design, and ongoing calibration.
High-performance GPUs are necessary but not sufficient. The system must be configured to handle multiple concurrent workloads, manage thermal and power constraints, and integrate cleanly with existing automation platforms.
Hardware Requirements
The current generation of NVIDIA RTX hardware (4090, 5090) provides sufficient compute for running multiple models simultaneously — vision, language, audio — without resource contention. For larger properties or more demanding workloads, the DGX Spark platform offers significantly higher capacity in a form factor designed for continuous operation.
Network Architecture
A local AI system needs reliable, low-latency access to cameras, microphones, sensors, and control interfaces throughout the property. This requires thoughtful network segmentation and quality of service configuration to ensure that AI workloads do not compete with other critical systems for bandwidth.
Perhaps most importantly, local AI benefits significantly from a calibration period. Initial model performance is rarely optimal without observation and refinement based on actual usage patterns. A system that is tuned to the specific behavior patterns of a property significantly outperforms one that relies on generic model configurations.
This is why we treat deployments as engineering projects rather than product installations. The hardware selection is straightforward. The configuration, integration, and calibration are what determine whether the system performs as expected.