---
slug: getting_started/installation/gpu
title: NVIDIA GPU
---
<a id="nvidia-gpu"></a>

Instructions to install FastVideo for NVIDIA CUDA GPUs.

## Requirements {#requirements}

- **OS: Linux or Windows WSL**
- **Python: 3.10-3.12**
- **CUDA: 12.6 or 13.0**
- **At least 1 NVIDIA GPU**

## Set up using Python {#set-up-using-python}
### Create a new Python environment {#create-a-new-python-environment}

#### uv {#uv}
Recommended default: use [uv](https://docs.astral.sh/uv/) for faster and more stable environment setup.

Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, create a new environment using:

```console
# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools`.
uv venv --python 3.12 --seed
source .venv/bin/activate
```

#### Conda (alternative) {#conda-alternative}
You can also create a Python environment using [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html).

##### 1. Install Miniconda (if not already installed) {#1-install-miniconda-if-not-already-installed}

```bash
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
source ~/.bashrc
```

##### 2. Create and activate a Conda environment for FastVideo {#2-create-and-activate-a-conda-environment-for-fastvideo}

```bash
# Create and activate a Conda environment
conda create -n fastvideo python=3.12 -y
conda activate fastvideo
```

### Installation {#installation}

#### With uv (recommended) {#with-uv-recommended}

```bash
UV_TORCH_BACKEND=cu126 uv pip install fastvideo
```

Also optionally install FlashAttention:

```bash
uv pip install flash-attn --no-build-isolation -v
```

#### With Conda environment (alternative) {#with-conda-environment-alternative}

`uv` works inside an active conda env too, so prefer `uv pip` for the actual install:

```bash
UV_TORCH_BACKEND=cu126 uv pip install fastvideo
```

Also optionally install FlashAttention:

```bash
uv pip install flash-attn --no-build-isolation -v
```

### Installation from Source {#installation-from-source}

#### 1. Clone the FastVideo repository {#1-clone-the-fastvideo-repository}

```bash
git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
```

#### 2. Install FastVideo {#2-install-fastvideo}

FastVideo requires PyTorch 2.12.0. Use `UV_TORCH_BACKEND=cu126` for CUDA 12 or
`UV_TORCH_BACKEND=cu130` for CUDA 13.

Basic installation:

```bash
UV_TORCH_BACKEND=cu126 uv pip install -e .
```

Alternative with Conda environment:

```bash
UV_TORCH_BACKEND=cu126 uv pip install -e .
```

### Optional Dependencies {#optional-dependencies}

#### Flash Attention {#flash-attention}

```bash
uv pip install flash-attn --no-build-isolation -v
```

Alternative with Conda environment:

```bash
uv pip install flash-attn --no-build-isolation -v
```

## Set up using Docker {#set-up-using-docker}
We also have prebuilt docker images with FastVideo dependencies pre-installed:
[Docker Images](/FastVideo/contributing/developer_env/docker)

## Development Environment Setup {#development-environment-setup}

If you're planning to contribute to FastVideo please see the following page:
[Contributor Guide](/FastVideo/contributing/overview)

## Hardware Requirements {#hardware-requirements}

### For Basic Inference {#for-basic-inference}
- NVIDIA GPU compatible with CUDA 12.6 or newer

### For Lora Finetuning {#for-lora-finetuning}
- 40GB GPU memory each for 2 GPUs with lora
- 30GB GPU memory each for 2 GPUs with CPU offload and lora

### For Full Finetuning/Distillation {#for-full-finetuningdistillation}
- Multiple high-memory GPUs recommended (e.g., H100)

## Troubleshooting {#troubleshooting}

If you encounter any issues during installation, please open an issue on our [GitHub repository](https://github.com/hao-ai-lab/FastVideo).

You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ) for additional support.