ActTune: Action-Aware Precision and GPU Operating-Point Adaptation for Energy-Efficient Vision-Language-Action Inference

Vision-language-action (VLA) policies repeatedly invoke inference to control robots, making graphics processing unit (GPU) energy a recurring cost of task execution. Reducing energy per inference call, however, may not reduce energy per successful task if numerical errors increase failures or slower inference prolongs execution. We therefore target GPU energy per successful task while preserving task success and limiting increases in episode duration. Our approach builds on two observations: quantization sensitivity varies across action classes, model layers, and weights versus activations; and numerical precision changes the workload, shifting favorable GPU operating points. We introduce ActTune, an action-aware framework that connects layer-wise precision allocation with workload-dependent GPU frequency selection. A lightweight recognizer selects precision before each policy call. The controller forecasts the next workload and applies GPU settings asynchronously using a lookup table calibrated under a latency budget. A shared resident quantized weight bank enables configuration switching without weight reconstruction or additional policy evaluations. On LIBERO, a benchmark for lifelong robot learning, ActTune with the evaluated selector improves mean task success by up to 2.4% relative to state of the art. Relative to the original BF16 implementations, it delivers up to $2.02\times$ faster inference and, with dynamic voltage and frequency scaling (DVFS), reduces energy per successful task by up to 76.8%.