We present SharpGS, a differentiable blur-driven density control, which enhances the quality of 3D Gaussian Splatting (3DGS). Standard 3DGS often struggles to capture intricate details, particularly in textured patterns and object boundaries, due to the limited sensitivity of its density control. Simple finer densification can result in excessive primitive counts with marginal quality gain. To address this, we introduce blur as an effective perceptual cue, leveraging CUDA-based differentiable blur. Our density control estimates per-primitive blur levels by comparing reconstructions to blurred ground-truth images, and identifies high-frequency regions where additional primitives are required. While this naturally produces more primitives, we counterbalance this by suppressing redundant primitives. Specifically, we penalize the blur levels of inherently smooth regions such as sky, and regularize the opacities of potential split/clone candidates. We experimentally demonstrate that SharpGS greatly improves the state-of-the-art 3DGS methods in terms of quality, while keeping learned primitives compact.