Practical overview
When it fits
Use Instant Ngp when a task involves training, rendering or interactively inspecting one of its supported neural graphics representations, rather than general-purpose image generation or a hosted AI service.
Who it is for
- Researchers and graphics developers working with learned scene or image representations.
- Users who want to train or inspect NeRF and SDF examples.
- Developers building workflows around neural graphics primitives on NVIDIA GPUs.
Documented uses
- Train and render a NeRF from a compatible image dataset.
- Load and explore an SDF representation from an OBJ model.
- Train or render neural images and neural volumes.
- Convert NeRFs or SDFs to meshes and create camera-path videos.
Requirements & limits
The project provides downloadable Windows builds for specified NVIDIA GPU generations. Linux use, Python bindings, and GPU generations outside the listed Windows builds require building from source.
- The documented workflows require an NVIDIA GPU; tensor cores can increase performance.
- The project uses CUDA and tiny-cuda-nn, so GPU generation and CUDA setup matter when choosing a build path.
- NeRF training requires a NeRF-compatible dataset; users creating their own can follow the linked dataset instructions.
- Python bindings, faster mesh SDF training and DLSS support have separate optional prerequisites.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Open the included NeRF fox example
Load and explore the project’s included NeRF sample using the GUI or command line.
Before you start
- An installed Instant Ngp application.
- The included data/nerf/fox sample.
Steps
Start Instant Ngp and drag the data/nerf/fox folder into the application window.
Alternatively, run ./instant-ngp data/nerf/fox from the application directory.
Expected outcome
The fox NeRF sample opens for interactive exploration.
Guide limitations
- The instructions describe the included sample; creating a NeRF from personal images requires the project’s separate dataset guidance.
Build Instant Ngp from source
Configure and compile the project on Windows or Linux.
Before you start
- An NVIDIA GPU.
- A C++14-capable compiler: Visual Studio 2019 or 2022 on Windows, or GCC/G++ 8 or later on Linux.
- CUDA 11.5 or later on Windows, or CUDA 10.2 or later on Linux.
- CMake 3.21 or later.
Steps
Clone the repository and its submodules with git clone --recursive https://github.com/nvlabs/instant-ngp.
Change into the instant-ngp directory.
Configure the build with cmake . -B build -DCMAKE_BUILD_TYPE=RelWithDebInfo.
Expected outcome
CMake configures a build in the build directory; the documented excerpt does not include the subsequent compilation command.
Guide limitations
- Python 3.8 or later is optional for interactive bindings, and requires the documented requirements file.
- OptiX 7.6 or later is optional for faster mesh SDF training; the Vulkan SDK is optional for DLSS support.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- NVlabs/instant-ngp · Official homepage title · retained in its source language