Can RTX 4080 SUPER run Wan 2.2 TI2V 5B?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 SUPER (16 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the Q8 quantization, which fits in 10.0 GB. Expected throughput is around 114 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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an NVIDIA GeForce RTX 4080 SUPER with a Grade S performance, using the Q8 quantization for optimal efficiency and speed (~72 tok/sec).
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 526.95 or later), and CUDA 11.8 installed.
Expected performance
With the Q8 quantization, you can expect a token generation rate of approximately 72 tok/sec, utilizing around 10.0GB of VRAM. This leaves you with 6.0GB of VRAM headroom, allowing for a practical context window that can handle longer sequences or more complex tasks.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. Download the model
Download the Q8 quantized model (5.0GB file) from Hugging Face.
ollama pull Wan-AI/Wan2.2-TI2V-5B-Q83. Run it
ollama run Wan2.2-TI2V-5B-Q8 --device cuda
ollama chat Wan2.2-TI2V-5B-Q84. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. Given the 16GB VRAM, you can set --tensor-parallelism to 2 to further optimize parallel processing without exceeding VRAM limits.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers (--n-gpu-layers) or enable flash attention (--flash-attn) to lower VRAM usage.
Slow token generation rate
Ensure CUDA is properly configured and try increasing the tensor parallelism (--tensor-parallelism 2).
Model fails to load
Verify that the model file has been downloaded correctly and that the Ollama runtime is up to date. Try re-downloading the model or reinstalling Ollama.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization. Ollama is recommended for its ease of use and efficient GPU utilization on the NVIDIA GeForce RTX 4080 SUPER.
Other models that run great on RTX 4080 SUPER
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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