Question 1

Where do I download TripoSR?

Accepted Answer

TripoSR is distributed from two official places. The source code is on GitHub at VAST-AI-Research/TripoSR, and the pretrained model weights are on Hugging Face at stabilityai/TripoSR. You do not have to fetch the weights by hand: the first time you run the model they are downloaded and cached automatically. Both the code and the weights are free under the MIT license, so you can use them for personal, research, and commercial projects.

Question 2

What are the GPU and VRAM requirements for TripoSR?

Accepted Answer

TripoSR is deliberately lightweight and runs on a single consumer GPU rather than a data-center card. It also ships a CPU only mode that works on machines without a supported GPU, though inference is noticeably slower there. Exact memory use depends on your settings and PyTorch version, so the official GitHub README is the authoritative place to check the current VRAM figure before installing. As a rule of thumb, a modern mid-range NVIDIA GPU is comfortable for single-image inference.

Question 3

How do I install TripoSR locally, step by step?

Accepted Answer

Clone the repository from GitHub, create a Python 3.8 or newer virtual environment, install PyTorch for your platform, then install the remaining dependencies with a single pip command against the project requirements file. Once that is done you run the included run.py on any image to produce a 3D mesh, and the first run downloads the pretrained weights automatically. The repository README lists the exact commands and a few example images to test your setup.

Question 4

Can I run TripoSR without a local install?

Accepted Answer

Yes, in two ways. You can run it on a free cloud GPU through a community Google Colab notebook, or you can use the free hosted demo on Triposr.org, which runs entirely in your browser with no Python, no GPU, and no downloads. The hosted demo is the fastest way to get a result, while Colab is useful when you want to run the original code without setting up your own machine.

Question 5

Is there a one click installer for TripoSR?

Accepted Answer

Yes. Pinokio provides a one click installer that sets up the Python environment and dependencies for you, which avoids the manual virtual-environment and pip steps. It is a good option if you want a local install but would rather not configure the environment by hand. After the one click setup, you use TripoSR exactly the same way as a manual install.

Question 6

What Python version and dependencies does TripoSR need?

Accepted Answer

TripoSR needs Python 3.8 or newer with PyTorch installed for your operating system and CUDA version. The rest of the dependencies are pinned in the project requirements file and install in one pip command. Using a dedicated virtual environment is recommended so the TripoSR dependencies do not conflict with other Python projects on your machine.

Question 7

Is TripoSR free for commercial use?

Accepted Answer

Yes. TripoSR is released under the MIT license, which permits commercial use, modification, and redistribution, including inside closed-source products, as long as you keep the license and copyright notice. Both the code on GitHub and the weights on Hugging Face fall under this license, so you can build and ship commercial applications on top of TripoSR. Review the LICENSE file in the repository for the exact terms.

Question 8

What output does TripoSR produce?

Accepted Answer

TripoSR reconstructs a textured 3D mesh from your input image. The reference run.py exports the result as a standard mesh file that you can open in common 3D tools such as Blender, and it can also render preview images of the result. For the exact export formats and command-line flags, see run.py and the README in the official repository.

Question 9

Does TripoSR have an API?

Accepted Answer

The open-source TripoSR model does not ship a hosted API: you run it yourself in Python and can wrap it in your own service. If you need a managed REST API, Tripo AI, the team behind TripoSR, offers the Tripo API for image-to-3D and text-to-3D generation in the cloud, with authentication and GLB or OBJ output. The free Triposr.org demo is browser-based and is not an API.

Question 10

How do I call TripoSR from my own code?

Accepted Answer

Install the open-source TripoSR with Python and PyTorch, load the model, and run inference on an image to get a mesh. To expose that over HTTP, wrap it in your own API with a framework such as FastAPI or Flask. If you would rather not host the model, call the managed Tripo API instead. Either way you get back a 3D mesh as an OBJ or GLB file.

Download and Install TripoSR

Official GitHub Repository

Model Weights on Hugging Face

No Install Needed

Download and requirements, answered

Can I download TripoSR for free?

What are the GPU and VRAM requirements?

How do I install TripoSR locally?

Is there a Colab or one click option?

Four ways to run TripoSR

Local install (full control)

Google Colab (no local GPU)

Pinokio (one click)

Hosted demo (zero setup)

Using TripoSR programmatically (API)

Self-host the open-source model

Tripo API (managed REST API)

TripoSR install FAQ

Skip the setup and start now