Can I run Seedance 2.0 locally on my PC?

As of February 2026, Seedance 2.0 model weights have not been publicly released by ByteDance. You cannot run the exact Seedance 2.0 model locally yet. However, earlier versions (Seedance 1.0, Lite) and similar open-source alternatives like Wan 2.1 can be run locally. The recommended approach for Seedance 2.0 is to use the API via Dreamina, BytePlus, or third-party wrappers.

What GPU do I need to run Seedance locally?

For open-source video generation models comparable to Seedance, you need at minimum an NVIDIA GPU with 16GB VRAM (RTX 4080 or A5000). For optimal quality, 24GB+ VRAM is recommended (RTX 4090, RTX A6000, or A100). AMD GPUs have experimental support via ROCm but NVIDIA CUDA is strongly recommended.

How much disk space does Seedance require?

Model weights alone typically require 20-50GB depending on the model version and precision (FP16 vs FP32). With dependencies, Python environment, and output storage, plan for at least 100GB of free disk space. SSD storage is strongly recommended for acceptable load times.

Can I run Seedance on macOS with Apple Silicon?

Apple Silicon (M1/M2/M3/M4) Macs can run some video generation models via MPS (Metal Performance Shaders) backend in PyTorch. Performance is significantly lower than NVIDIA CUDA GPUs. A Mac with 32GB+ unified memory is the minimum. For production use, a Linux machine with an NVIDIA GPU is strongly recommended.

Run Seedance Locally — Complete Installation Guide [2026]

IMPORTANT NOTE

Current Availability (Feb 2026)

Before proceeding, understand what can and cannot be run locally right now.

Seedance 2.0 is NOT available for local inference. ByteDance has not released the model weights publicly. The guide below covers: (1) how to set up API-based local scripts that call Seedance remotely, (2) running open-source alternatives locally, and (3) preparing your setup for when weights become available. See our open source status page for the latest.

✗

Seedance 2.0

Not available locally. Weights are proprietary. Use Dreamina or API for Seedance 2.0 access.

✓

API Scripts (Local)

Run Python/Node.js scripts locally that call the Seedance API. Full control, batch automation, no Dreamina UI needed.

✓

Open Source Alternatives

Wan 2.1/2.6, CogVideo, and other open models run fully locally. Similar workflow, different model architecture.

SYSTEM REQUIREMENTS

Hardware & Software Specs

Requirements for running AI video generation models locally. These specs apply to open-source models similar to Seedance and will apply to Seedance itself when weights become available.

Component	Minimum	Recommended	Optimal
GPU	RTX 3090 (24GB)	RTX 4090 (24GB)	A100 80GB / H100
VRAM	16GB (720p only)	24GB	48-80GB
System RAM	16GB	32GB	64GB+
Storage	100GB SSD	250GB NVMe SSD	500GB+ NVMe
CPU	8 cores (Ryzen 5 / i7)	12+ cores	16+ cores
OS	Windows 10/11, Ubuntu 22.04+	Ubuntu 22.04 LTS	Ubuntu 22.04/24.04 LTS
CUDA	11.8+	12.1+	12.4+
Python	3.10	3.10-3.11	3.11

Cloud GPU option: If you lack a local GPU, rent one. RunPod offers RTX 4090 instances at ~$0.44/hr and A100 80GB at ~$1.64/hr. Vast.ai often has cheaper spot pricing. This lets you run the full local setup without owning hardware.

INSTALLATION

Python Environment Setup

Set up a clean Python environment for AI video generation. This base setup works for Seedance API scripts and open-source alternatives.

Step 1: Install System Dependencies

# Ubuntu/Debian
sudo apt update && sudo apt install -y python3.11 python3.11-venv git wget ffmpeg

# Install NVIDIA CUDA Toolkit (if not already installed)
# Follow: https://developer.nvidia.com/cuda-downloads

# Verify GPU is detected
nvidia-smi

Step 2: Create Virtual Environment

# Create project directory
mkdir ~/seedance-local && cd ~/seedance-local

# Create and activate virtual environment
python3.11 -m venv venv
source venv/bin/activate

# Upgrade pip
pip install --upgrade pip setuptools wheel

Step 3: Install PyTorch with CUDA

# PyTorch with CUDA 12.1 (adjust for your CUDA version)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Verify CUDA is available
python -c "import torch; print(torch.cuda.is_available())"
# Should print: True

Step 4: Install Video Generation Dependencies

# Common dependencies for video generation models
pip install diffusers transformers accelerate safetensors
pip install imageio imageio-ffmpeg opencv-python pillow
pip install einops omegaconf decord

# For Seedance API scripts
pip install requests httpx aiohttp

Step 5: Download Models (Open Source Alternative)

# Example: Download Wan 2.1 (open source, similar to Seedance)
pip install huggingface_hub
huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir models/wan2.1

# This will download ~28GB of model weights
# For Seedance weights (when available), the command will be similar

DOCKER

Docker Setup Option

Docker provides a reproducible, isolated environment. This is the recommended approach for production deployments and cloud GPU instances.

Dockerfile

FROM nvidia/cuda:12.1.1-devel-ubuntu22.04

# System dependencies
RUN apt-get update && apt-get install -y \
    python3.11 python3.11-venv python3-pip \
    git wget ffmpeg && \
    rm -rf /var/lib/apt/lists/*

WORKDIR /app

# Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Model weights (mount as volume in production)
VOLUME /app/models
VOLUME /app/output

COPY . .
CMD ["python", "generate.py"]

Run with Docker Compose

# docker-compose.yml
version: '3.8'
services:
  seedance:
    build: .
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - SEEDANCE_API_KEY=${SEEDANCE_API_KEY}
    volumes:
      - ./models:/app/models
      - ./output:/app/output
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Run with: docker compose up --build

PERFORMANCE

Performance Optimization Tips

Squeeze maximum performance from your hardware for faster video generation.

⚡

Use FP16 / BF16 Precision

Always load models in half precision (torch.float16 or torch.bfloat16). This halves VRAM usage with negligible quality loss. BF16 is preferred on Ampere+ GPUs (RTX 30/40 series, A100).

💾

Enable Model Offloading

Use accelerate with CPU offloading to handle models larger than your VRAM. Components are moved to CPU RAM when not in use. Slower than full GPU, but allows running larger models.

📈

Optimize with torch.compile

Enable torch.compile(model) for 10-30% speedup on PyTorch 2.0+. The first generation will be slower (compilation), but subsequent runs benefit significantly. Works best on Linux.

🔌

NVMe Storage for Weights

Store model weights on NVMe SSD, not HDD. Model loading from HDD can take 60+ seconds vs 5-10 seconds from NVMe. This matters for workflows with frequent model switches or cold starts.

🎈

Start Small, Scale Up

Generate at 480p or 720p first to validate prompts quickly. A 480p 5s video generates 4-8x faster than 1080p 10s. Scale up only for final production renders. This is the single biggest time-saver.

🛠

Use xformers / Flash Attention

Install xformers for memory-efficient attention. Flash Attention 2 provides 2-4x speedup on supported GPUs. Run pip install xformers and ensure your model code uses it.

TROUBLESHOOTING

Common Errors & Fixes

CUDA out of memory (OOM)

Reduce resolution or video duration. Enable FP16 precision. Enable CPU offloading via accelerate. Close other GPU applications. If using a 16GB GPU, try 480p 5s first. For 24GB GPUs, 720p 10s should work in FP16.

torch.cuda.is_available() returns False

Your PyTorch installation does not detect the GPU. Verify NVIDIA drivers with nvidia-smi. Ensure you installed PyTorch with CUDA support (not the CPU-only version). Check that your CUDA toolkit version matches the PyTorch build. Reinstall: pip install torch --index-url https://download.pytorch.org/whl/cu121

Model download stuck or corrupted

HuggingFace downloads can fail on unstable connections. Use huggingface-cli download --resume-download to resume. Verify file integrity with sha256sum against the model card checksums. For large models (20GB+), use aria2c for multi-connection downloads.

ImportError: No module named 'diffusers'

You are not in the correct virtual environment. Run source venv/bin/activate first. If using Docker, ensure the requirements.txt includes all dependencies. Run pip install diffusers transformers accelerate to install missing packages.

Output video is black or has artifacts

Common with FP16 on older GPUs that lack proper half-precision support. Try FP32 instead (uses more VRAM but avoids precision issues). Check that FFmpeg is installed and up to date. Ensure output tensor values are correctly scaled to 0-255 before encoding.

Docker: "could not select device driver" GPU error

Install the NVIDIA Container Toolkit: sudo apt install nvidia-container-toolkit. Restart Docker: sudo systemctl restart docker. Verify with docker run --gpus all nvidia/cuda:12.1.1-base nvidia-smi. Ensure your Docker version supports the --gpus flag (19.03+).

FAQ

Local Installation Questions

As of February 2026, Seedance 2.0 model weights have not been publicly released by ByteDance. You cannot run the exact Seedance 2.0 model locally yet. For Seedance 2.0 access, use Dreamina, the BytePlus API, or third-party API wrappers. For local inference, consider open-source alternatives like Wan 2.1 which has comparable capabilities.

For open-source video generation models comparable to Seedance: minimum 16GB VRAM (RTX 4080, A5000) for 720p output, recommended 24GB VRAM (RTX 4090, RTX A6000) for 1080p. AMD GPUs have experimental ROCm support but NVIDIA CUDA is strongly recommended for compatibility and performance.

Apple Silicon Macs can run some video generation models via PyTorch's MPS (Metal Performance Shaders) backend. Performance is 3-5x slower than equivalent NVIDIA GPUs. You need at least 32GB unified memory. M3 Max/Ultra and M4 Pro/Max with 48GB+ memory provide the best experience. For production workloads, a Linux machine with an NVIDIA GPU is strongly recommended.

Generation time depends heavily on hardware, model, resolution, and duration. On an RTX 4090 with a 14B parameter model: ~30-60s for a 5s 720p video, ~90-180s for a 10s 1080p video. On cloud A100 80GB: roughly 50% faster. On Apple M3 Max: 3-5x slower than RTX 4090. API-based generation (Dreamina/BytePlus) is typically faster due to optimized inference infrastructure.

It depends on volume. If you generate fewer than ~100 videos/month, API pricing is more cost-effective (no hardware investment). For heavy usage (500+ videos/month), local generation on owned hardware becomes cheaper over time. Cloud GPU rental (RunPod ~$0.44/hr for RTX 4090) is a good middle ground — you pay only for compute time without hardware ownership costs.

Run Seedance Locally