Abstract: Recently the remarkable progress in 3D Gaussian Splatting (3DGS) has demonstrated huge potential over traditional rendering techniques, attracting significant attention from both industry and academia. Due to the presence of numerous anisotropic Gaussian representations in large-scale and high-resolution scenes, real-time rendering with 3DGS remains a challenging problem and is also rarely studied. We proposed FlashGS, an open-source CUDA library with Python bind, with comprehensive algorithm design and optimizations, encompassing redundancy elimination, adaptive scheduling, and efficient pipelining. We first eliminate substantial redundant tasks through precise Gaussian intersection tests, considering the essence of the 3DGS rasterizer. During task partitioning, we introduce an adaptive scheduling strategy that accounts for variations in the size and shape of Gaussians. We also design a multi-stage pipeline strategy for color computations in rendering, further accelerating the process. An extensive evaluation of FlashGS has been conducted across a diverse spectrum of synthetic and real-world 3D scenes, covering a variety of scene sizes up to 2.7 km$^2$ cityscape and resolutions up to 4K. We achieve up to 30.53$\times$ faster than 3DGS with an average of $7.2\times$, rendering at a minimum of 125.9 FPS, achieving state-of-the-art performance.