3D Gaussian Splatting (3D-GS) has recently emerged as a powerful technique for real-time, photorealistic rendering by optimizing anisotropic Gaussian primitives from view-dependent images. Unlike neural implicit methods such as NeRF, 3D-GS avoids the need for a neural network forward pass at inference, making it significantly faster while maintaining high visual fidelity. While 3D-GS has been extended to scientific visualization, prior work remains limited to single-GPU settings, restricting scalability for large scientific datasets on high-performance computing (HPC) systems. In this study, we present a distributed 3D-GS pipeline tailored for scientific data on HPC. Our approach partitions data across nodes, trains Gaussian splats in parallel using multi-nodes and multi-GPUs, and merges splats for global rendering. To eliminate artifacts, we add ghost cells at partition boundaries and apply background masks to remove irrelevant pixels. Benchmarks on the Richtmyer–Meshkov datasets (about 106.7M Gaussians) show up to 3X speedup across 8 nodes on Polaris while preserving image quality. These results demonstrate that distributed 3D-GS enables scalable visualization of large-scale scientific data and provides a foundation for future in situ applications.