  In recent times, the utilization of 3D models has gained traction, owing to
the capacity for end-to-end training initially offered by Neural Radiance
Fields and more recently by 3D Gaussian Splatting (3DGS) models. The latter
holds a significant advantage by inherently easing rapid convergence during
training and offering extensive editability. However, despite rapid
advancements, the literature still lives in its infancy regarding the
scalability of these models. In this study, we take some initial steps in
addressing this gap, showing an approach that enables both the memory and
computational scalability of such models. Specifically, we propose "Trimming
the fat", a post-hoc gradient-informed iterative pruning technique to eliminate
redundant information encoded in the model. Our experimental findings on widely
acknowledged benchmarks attest to the effectiveness of our approach, revealing
that up to 75% of the Gaussians can be removed while maintaining or even
improving upon baseline performance. Our approach achieves around 50$\times$
compression while preserving performance similar to the baseline model, and is
able to speed-up computation up to 600 FPS.
