Modern AI vision deployments behave like continuous dataflow systems: thousands of camera streams require repeated data processing on the CPU before any neural network can run on the GPU. In multi-model DAG pipelines, these data processing steps multiply across stages, consuming significant CPU cycles and leaving GPUs underutilized. The CPU-bound nature of these tasks limits overall throughput, increases latency, and forces costly over-provisioning.