The deployment of demanding computer vision algorithms on embedded devices is a complicated challenge due to limited memory, computational capabilities and energy constraints. In many low-latency robotic and autonomous applications, edge deployment is indispensable, as real-time response cannot rely on remote servers. This work focuses on the optimization of the Variational Bayes Gaussian Splatting algorithm, a state-of-the-art probabilistic 3D scene modelling method originally developed for server-grade GPUs with more than 20 GBs of RAM and not previously implemented on edge platforms. We target the NVIDIA Jetson Orin Nano, an embedded GPU platform with just 8 GBs of RAM. Rather than modifying the mathematical model itself, this work aims at optimizing the existing implementation to meet the edge devices limitations and improve the overall latency.