Systems | Development | Analytics | API | Testing

AI sovereignty has become one of those phrases that sounds precise until someone asks what it actually means. For one federal agency, sovereignty means keeping sensitive data inside accredited boundaries. For another, it means running open-weight models in a FedRAMP-authorized private cloud. In defense and intelligence settings, it may mean operating inside an air-gapped environment at IL5 or IL6.

Almost every infrastructure team running modern AI is wrestling with the same question. The orchestrator their AI workloads want, Kubernetes, and the orchestrator their HPC environment was built on, Slurm, pull in different directions. This piece, drawn from the HPCKP 2026 session of the same name, looks at why that tension exists, what the workloads actually look like, how teams are bridging the two today, and where the pattern is heading.

Training models for physical robots is harder than training a typical model. The data has to be collected by hand through teleoperation, every change has to be tested on real hardware, and the loop from data to deployment runs constantly. In a recent proof of concept with a Singapore government agency, ClearML, Dell Technologies, and Hugging Face’s LeRobot framework turned that high-touch, manual process into an automated pipeline.