# GEMM-GS: Accelerating 3D Gaussian Splatting on Tensor Cores with GEMM-Compatible Blending

arXiv: https://arxiv.org/abs/2604.02120 · HTML: https://mlsanity.com/paper/2604.02120/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2026.

## Built on by (most cited first, 2 of 2)

- [Local-GS: Accelerating 3D Gaussian Splatting via Tile-Local Warp Coherence](https://mlsanity.com/paper/2606.16566/) (2026)
- [RoofGS: Roofline-Guided End-to-End Acceleration of 3D Gaussian Splatting](https://mlsanity.com/paper/2608.15785/) (2026)

## Abstract (arXiv)

Neural Radiance Fields (NeRF) enables 3D scene reconstruction from several 2D images but incurs high rendering latency via its point-sampling design. 3D Gaussian Splatting (3DGS) improves on NeRF with explicit scene representation and an optimized pipeline yet still fails to meet practical real-time demands. Existing acceleration works overlook the evolving Tensor Cores of modern GPUs because 3DGS pipeline lacks General Matrix Multiplication (GEMM) operations. This paper proposes GEMM-GS, an acceleration approach utilizing tensor cores on GPUs via GEMM-friendly blending transformation. It equivalently reformulates the 3DGS blending process into a GEMM-compatible form to utilize Tensor Cores. A high-performance CUDA kernel is designed, integrating a three-stage double-buffered pipeline that overlaps computation and memory access. Extensive experiments show that GEMM-GS achieves 1.42× speedup over vanilla 3DGS and provides an additional 1.47× speedup on average when combining with existing acceleration approaches. Code is released at https://github.com/shieldforever/GEMM-GS.