AI Inference Latency

The time elapsed between a robotic system receiving sensor data and completing the AI-based computation needed to generate an action decision.

AI inference latency is the computational delay between when a sensor captures data and when the AI model produces an actionable output - a navigation direction, a grasp plan, an object classification. In mobile robotics, inference latency directly affects the robot’s ability to respond to dynamic environments: a robot moving at 1.5 m/s with 200ms inference latency has traveled 30cm before its last perception update has produced an output, creating a position uncertainty that accumulates in complex traffic environments. Latency is managed through model optimization (quantization, pruning), hardware selection (onboard GPUs, dedicated inference chips), and edge deployment that avoids round-trip network delays. For automation deployments where safety or performance is latency-sensitive, inference latency should be evaluated under operational load conditions, not quoted from single-sample benchmarks.

Related terms: Edge Computing · Sensor Fusion · System Uptime