KULR discusses trends toward integrating agentic AI workloads into battery management systems.
“Four trends point in this direction. Edge inference silicon is shrinking fast — a Jetson Orin Nano delivers 40 trillion operations per second at 15 watts, smaller than a deck of cards. Small language models are advancing toward distilled forms that run on hardware fitting inside a battery enclosure. Agentic workloads — predictive maintenance, anomaly detection, energy optimization — operate on exactly the current, voltage, temperature, and cycle data the battery management system already holds, making the BMS their natural home. As compute becomes the smaller element, the rational configuration is compute inside the power system, not power beside it — and the owner of the power infrastructure becomes the natural integration point for the compute, memory, and intelligence that run on top of it.”