Physical AI Networks: How LLM Inference Pipelines Drive Robot Intelligence

This chapter from the STATERA Library's robotics communication series examines how LLM inference pipelines function within Physical AI systems such as mobile robots, humanoids, and industrial inspection platforms. It describes the pipeline as a distributed architecture linking sensors, edge computers, GPU accelerators, middleware, cloud services, and actuators rather than a standalone software module. The pipeline begins with input acquisition from operators, fleet systems, or sensors, followed by preprocessing such as tokenization and encoding of multimodal data. The author emphasizes that tokenization strategies strongly influence latency, memory use, and throughput, which matter for real-time industrial deployments.