Finding
Intelligence moves into the machine because physical control loops cannot wait for a network round trip
A car at 70 mph covers about 3 meters in 100 milliseconds - longer than many cloud round trips take, and far too long for braking or balance. So the AI that drives, walks, balances a battery or protects a grid connection runs inside the machine or at the site, on embedded inference chips. The cloud still trains the models and handles fleet-wide analysis, but the decisions that move hardware happen locally.
That shift multiplies the number of AI computers: every vehicle, robot, charger and battery system carries one. It ties AI growth to automotive-grade chips, memory and power supplies, and to the energy and heat budget inside each machine - a humanoid has a battery of 1-3 kWh to power both motors and compute. See Edge Inference Compute and Autonomy Compute Platforms.
Tiers
Where inference runs
Response times are typical ranges.
| Tier | Response time | Runs | Examples |
|---|---|---|---|
| Device | < 10 ms | Perception, control, safety | Vehicle autonomy computer, robot controller, BMS |
| Site edge | ~10-30 ms | Coordination across assets | Depot or microgrid controller |
| Regional cloud | ~30-100 ms | Fleet dispatch, monitoring | Robotaxi dispatch |
| Central cloud | ~100-300 ms | Training, analytics | Model training, digital twins |
Sources
Sources and scope
- Scope
- Embedded Intelligence
- Sources
- ElectronsX analysis; typical network latency ranges
- Updated
- October 2026