# nerfstudio-project/gsplat

CUDA accelerated rasterization of gaussian splatting

Repository: https://github.com/nerfstudio-project/gsplat
Canonical: https://ross.abutalabs.com/products/gsplat
Homepage: https://docs.gsplat.studio/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: gaussian-splatting
Last push: 2026-08-20T20:44:25+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 28, longevity 78
- inputs: {"age_days": 1104, "days_push": 13, "days_rel": 425, "gap_med": 38.5, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5589, forks 946 (observed 2026-08-28T04:09:22.635046+00:00)

## What it is
gsplat is an open-source Python library with CUDA-accelerated, differentiable rasterization of Gaussians, based on 3D Gaussian Splatting for real-time radiance field rendering. It offers faster, more memory-efficient kernels with features like multi-GPU support, sparse rendering, and various camera/sensor models.

## Use cases
- render 3D gaussian splatting scenes in real time
- train radiance fields from photos with differentiable rasterization
- reconstruct 3D scenes from images using CUDA-accelerated splatting
- run multi-GPU distributed gaussian splatting training
- render with fisheye, LiDAR, or FTheta camera models
- speed up nerfstudio-style novel view synthesis

## When to choose
- you need fast, memory-efficient gaussian splatting rasterization on NVIDIA GPUs
- you want a maintained, feature-rich alternative to the original 3DGS implementation
- you need differentiable rendering integrated into PyTorch training pipelines
- you require multi-GPU or sparse rendering support

## When to avoid
- you have no CUDA-capable NVIDIA GPU
- you need a turnkey application rather than a Python library
- your project targets CPU-only rendering

## Facets
- artifact type: library
- maturity: active
- function: graphics, image-processing, machine-learning, gpu-computing
- domain: computer-vision, graphics, machine-learning, gpu-computing
- platform: python, cross-platform
- tags: gaussian-splatting, cuda, rasterization, neural-rendering, radiance-fields, differentiable-rendering, 3d-reconstruction, gpu, linux

## Member repositories
- nerfstudio-project/gsplat (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:22.635046+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:55:31.276031+00:00, confidence not recorded.
  - readme: https://github.com/nerfstudio-project/gsplat (fetched 2026-08-28T04:09:22.635046+00:00, sha 8a3660e52a10)
  - homepage: https://docs.gsplat.studio/ (fetched 2026-08-29T08:50:26.901763+00:00, sha bd23a3dcc217)
  - registry_pypi: https://pypi.org/pypi/gsplat/json (fetched 2026-08-29T08:50:26.904292+00:00, sha 73e48316a3de)
- Data as of 2026-08-30T08:39:29.467469+00:00.