3D Gaussian Splatting (3DGS) is an emerging neural rendering technique that delivers efficient and high-fidelity rendering, meeting the growing demands of applications such as AR/VR. As 3DGS is increasingly integrated into diverse applications, DNNs are often deployed alongside it to support tasks such as skeletal pose estimation for human avatars or semantic processing for 3D perception. Unfortunately, existing domain-specific accelerators (DSAs) designed for 3DGS excel at rendering but struggle to execute DNN workloads efficiently. Moreover, these DSAs incur significant design and fabrication costs, limiting their practicality. To address these challenges, we propose ORANGE, a novel approach that enables general-purpose DNN-oriented Neural Processing Units (NPUs) to efficiently execute 3DGS without requiring specialized accelerators. The key insight of ORANGE is that we introduce a GEMM-friendly blending process, which reformulates the conventional 3DGS blending operation to fully utilize the matrix multiplication units prevalent in NPUs during rendering. Additionally, to mitigate workload imbalances caused by variable execution latencies across tiles, we develop a sampling-based latency prediction method paired with a tile batching strategy to minimize idle computing resources. Experiments demonstrate that ORANGE achieves up to $1.67\times$ and $15.5\times$ speedup compared to state-of-the-art 3DGS accelerators and the NVIDIA Xavier NX GPU, respectively, in neural rendering tasks. Our approach offers a cost-effective and versatile solution, adhering to the principle of Ockham’s Razor by maximizing efficiency without specialized hardware.