🔧 Installation

Start with Hardware and runtimes to distinguish NVIDIA RTX and data center GPUs, DGX Spark, Apple Silicon MLX, and AMD ROCm.

The installation paths below cover:

NVIDIA CUDA
NVIDIA DGX Spark / GB10 (ARM64 + CUDA 13) — install, performance, pair two Sparks
Apple silicon (MLX)
AMD / ROCm support and current limits

Quick Installation

Using uv (recommended)

Use uv as the default environment manager for faster and more stable installs. The commands below target NVIDIA CUDA 12; use UV_TORCH_BACKEND=cu130 on CUDA 13. Apple silicon users should follow the MLX install guide.

# Create and activate a new uv environment
uv venv --python 3.12 --seed
source .venv/bin/activate

UV_TORCH_BACKEND=cu126 uv pip install fastvideo

Using Conda (alternative)

# Create and activate a new conda environment
conda create -n fastvideo python=3.12 -y
conda activate fastvideo

UV_TORCH_BACKEND=cu126 uv pip install fastvideo

From source

FastVideo pins PyTorch 2.12.0. Select its CUDA build explicitly with UV_TORCH_BACKEND=cu126 for CUDA 12 or UV_TORCH_BACKEND=cu130 for CUDA 13.

git clone https://github.com/hao-ai-lab/FastVideo.git
cd FastVideo
UV_TORCH_BACKEND=cu126 uv pip install -e .

# optional: install flash-attn
uv pip install flash-attn --no-build-isolation -v

Alternative with Conda environment (still drives installs through uv):

UV_TORCH_BACKEND=cu126 uv pip install -e .
uv pip install flash-attn --no-build-isolation -v

Install with an AI coding agent

FastVideo is a monorepo with rich agent guidance (see AGENTS.md). If you use Claude Code, Cursor, or another coding agent, paste the prompt below; it detects your platform and follows the matching guide:

Install FastVideo (https://github.com/hao-ai-lab/FastVideo) into a fresh uv virtual environment.

1. Detect the platform: run `uname -m`, `nvidia-smi`, and `nvcc --version`.
2. Read and follow the matching install guide exactly (in this repo, or at
   https://hao-ai-lab.github.io/FastVideo/getting_started/installation/):
     - NVIDIA GPU, x86_64                         -> docs/getting_started/installation/gpu.md
     - NVIDIA DGX Spark / GB10, aarch64, CUDA 13  -> docs/getting_started/installation/spark.md
     - Apple Silicon, macOS                       -> docs/getting_started/installation/mlx.md
3. Use uv for every step. If a command fails, debug it and tell me what you changed.
4. Verify the result:
     python -c "import fastvideo, torch; print('cuda', torch.cuda.is_available())"
     fastvideo --help
5. Report which platform you detected and any deviations you had to make.

Requirements

Python: 3.10-3.12 is the tested and recommended range (the commands above pin 3.12)
NVIDIA GPUs: CUDA 12.6+ with compute capability 7.0+
Apple Silicon: macOS 14.0+ with M1/M2/M3/M4 chips
CPU: x86_64 architecture (for CPU-only inference)

Next Steps

Quick Start - Generate your first video
Inference Cookbook - Choose a maintained recipe
Configuration - Learn about configuration options
Examples - Explore scripts and notebooks

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