Can RTX 3080 Ti run Wan 2.2 TI2V 5B?
Yes — runs locally
~74 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3080 Ti (12 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the Q8 quantization, which fits in 10.0 GB. Expected throughput is around 74 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Open-weights text-to-video and image-to-video model. Generates 5-second 480p clips on a single 24 GB card. The current open-source video sweet spot.
Setup tutorial: Wan 2.2 TI2V 5B on RTX 3080 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an NVIDIA GeForce RTX 3080 Ti with Q8 quantization for Grade A performance at ~54 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible OS (Windows or Linux), the latest NVIDIA drivers (version 512.15 or later), and CUDA 11.7 installed.
Expected performance
With the recommended settings, you can expect the model to run at approximately 54 tokens per second, using around 10.0GB of VRAM, leaving 2.0GB of headroom for context. This allows for a practical context window of several seconds of video, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8 quantized version of Wan 2.2 TI2V 5B (5.0GB file) from Hugging Face.
ollama pull Wan-AI/Wan2.2-TI2V-5B-Q83. Run it
ollama run Wan2.2-TI2V-5B-Q8 --n-gpu-layers 12 --flash-attn --tensor-parallelism 14. Optimize for RTX 3080 Ti
For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use --n-gpu-layers 12 to offload layers to the GPU, enable --flash-attn for faster attention computation, and set --tensor-parallelism 1 to avoid splitting the model across multiple GPUs. This configuration ensures that the model runs efficiently within the 12GB VRAM limit.
Troubleshooting
Out of memory errors during inference.
Reduce the number of --n-gpu-layers to 8 or 6 to lower VRAM usage.
Slow inference speed.
Ensure that --flash-attn is enabled and try increasing --tensor-parallelism to 2 if you have a multi-GPU setup.
Inference fails with CUDA errors.
Update your NVIDIA drivers to the latest version and reinstall CUDA 11.7.
Alternative runtimes
For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 3080 Ti.
Other models that run great on RTX 3080 Ti
FAQ (20)
What GPU do I need to run Wan 2.2 TI2V 5B?
To run Wan 2.2 TI2V 5B, you need a GPU with at least 10 GB of VRAM. For optimal performance, a GPU with 16 GB or more is recommended.
Is Wan 2.2 TI2V 5B good for coding?
Wan 2.2 TI2V 5B is primarily designed for generating video content, not for coding tasks. It may not be suitable for code generation or programming assistance.
Wan 2.2 TI2V 5B vs Llama 3.1 8B?
Wan 2.2 TI2V 5B is a 5B parameter model focused on video generation, while Llama 3.1 8B is a larger language model with 8B parameters, better suited for text-based tasks.
Can I run Wan 2.2 TI2V 5B on a Mac?
Yes, you can run Wan 2.2 TI2V 5B on a Mac as long as your Mac has a compatible GPU with at least 10 GB of VRAM.
How much VRAM does Wan 2.2 TI2V 5B need?
Wan 2.2 TI2V 5B requires between 10.0 GB and 16.0 GB of VRAM, depending on the quantization level used.
Is Wan 2.2 TI2V 5B censored?
Wan 2.2 TI2V 5B is not inherently censored, but it may include content filters to prevent the generation of inappropriate content.
Is Wan 2.2 TI2V 5B commercial-use allowed?
Yes, Wan 2.2 TI2V 5B is licensed under Apache-2.0, which allows for commercial use without additional fees.
Wan 2.2 TI2V 5B context length?
The context length for Wan 2.2 TI2V 5B is currently unknown, as it is not specified in the model documentation.
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