Why does Physical AI demand such massive computational power?

The answer lies in the fundamental constraints of real-world operation. These systems aren't sitting idle waiting for responses—they're constantly juggling multiple demanding tasks simultaneously.

First, there's the relentless stream of sensory input. Vision feeds, lidar data, accelerometers, tactile sensors—all flooding in continuously. Processing this raw data alone requires serious horsepower.

Then comes the decision-making pressure. We're talking millisecond-level response times. A robot navigating obstacles or an autonomous vehicle reacting to road conditions can't afford latency. There's no luxury of offloading to distant cloud servers and waiting. Every microsecond counts.

Beyond immediate reaction, these systems run inference constantly—not just once per second, but continuously evaluating their environment and adjusting behavior. And they're not static; they learn and adapt in real time, updating their models based on new experiences.

This is why on-device compute is non-negotiable. Physical intelligence isn't a cloud game. It's local, it's immediate, and it's hungry for processing power.