3DGS implementation in Nvidia Warp: clean, minimal, runs on CPU and GPU

In plain words

This is a clean, minimal implementation of 3D Gaussian Splatting built with NVIDIA Warp that runs on both CPU and GPU without requiring separate CUDA setup or device-specific code. Designed for developers and students learning differentiable rendering and parallel graphics programming, it prioritizes clarity over complexity, making it suitable for study, prototyping, and teaching without demanding expensive hardware or extensive boilerplate code.

written from the facts on this page · September 2026

From the sources

Build 3D Gaussian Splatting from scratch with NVIDIA Warp in Python — CPU/GPU compatible, with a clean and minimalist design focused on learning modern graphics.

Build 3D Gaussian Splatting from scratch with NVIDIA Warp in Python — CPU/GPU compatible, with a clean and minimalist design focused on learning modern graphics. - guoriyue/3dgs-warp-scratch

This project reimplements the core ideas of 3D Gaussian Splatting in a clean, minimalist Python codebase using NVIDIA Warp. It runs on both CPU and GPU with no CUDA setup, focuses on clarity and parallelism, and is designed as a practical entry point for learning modern graphics and differentiable rendering. Thanks to Warp, the same kernel code runs seamlessly on both CPU and GPU — no need to deal with CUDA setup, driver issues, or device-specific kernels. Just flip one config line. Explore differentiable rendering and parallel graphics through clean, readable Python — no pricey GPUs, complex toolchains, or heavy C++/CUDA boilerplate needed. This isn’t another massive codebase. It’s…from github.com

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