In recent years we are witnessing an increasing adoption of RISC-V based systems to run Artificial Intelligence (AI) inference tasks. This trend spans to visual navigation, where major players start adopting RISC-V for autonomous driving. Still, RISC-V based edge devices fall short in providing the performance requirements of complex AI inference. Our work tackles the previous challenges by proposing an opensource framework for transparent distribution of visual navigation inference tasks between edge and cloud for resource-constrained RISC-V edge devices. Our framework automates the partitioning of ONNX and TFLite models between a RISC-V accelerated nanodrone equipped a GAP8 system-on-chip and a cloud server. Our results showcase how partial inference improves the performance achieve by drone-only inference.