The efficiency improvements of gsplat, including reduced memory usage and faster training times, make it particularly well-suited for deployment in resource-constrained environments like mobile devices and web-based platforms. By leveraging optimized CUDA kernels and a lightweight PyTorch interface, gsplat can facilitate real-time rendering and 3D scene reconstruction on devices with limited computational power. This opens up new applications such as augmented reality (AR) and virtual reality (VR) experiences that require quick rendering of 3D scenes without compromising quality. Additionally, the modular design allows developers to tailor the library to specific hardware capabilities, optimizing performance for various devices. The ability to run Gaussian Splatting efficiently on mobile platforms could lead to innovative applications in gaming, remote collaboration, and interactive education, where users can engage with 3D content seamlessly in their everyday environments.