Abstract: Despite its significant achievements in large-scale scene reconstruction, 3D Gaussian Splatting still faces substantial challenges, including slow processing, high computational costs, and limited geometric accuracy.These core issues arise from its inherently unstructured design and the absence of efficient parallelization.To overcome these challenges simultaneously, we introduce \textbf{CityGS-\(\mathcal{X}\)}, a scalable architecture built on a novel parallelized hybrid hierarchical 3D representation (PH$^2$-3D).As an early attempt, CityGS-\(\mathcal{X}\) abandons the cumbersome merge-and-partition process and instead adopts a newly-designed batch-level multi-task rendering process. This architecture enables efficient multi-GPU rendering through dynamic Level-of-Detail voxel allocations, significantly improving scalability and performance.%To further enhance both overall quality and geometric accuracy, CityGS-\(\mathcal{X}\) presents a progressive RGB-Depth-Normal training strategy.This approach enhances 3D consistency by jointly optimizing appearance and geometry representation through multi-view constraints and off-the-shelf depth priors within batch-level training.%Through extensive experiments, CityGS-\(\mathcal{X}\) consistently outperforms existing methods in terms of faster training times, larger rendering capacities, and more accurate geometric details in large-scale scenes. %Notably, CityGS-\(\mathcal{X}\) can train and render a 5,000+ image scene with only 4\(\times\)4090 GPUs in just 5 hours, while many other methods even encounter Out-Of-Memory (OOM) issues during rendering, making CityGS-\(\mathcal{X}\) a more accessible and scalable solution for this task.Notably, CityGS-\(\mathcal{X}\) can train and render a scene with 5,000+ images in just 5 hours using only 4×4090 GPUs, a task that would make other alternative methods encounter Out-Of-Memory (OOM) issues and fail completely. This implies that CityGS-\(\mathcal{X}\) is far beyond the capacity of other existing methods.