Can RTX 3090 Ti run Wan 2.2 TI2V 5B?
Yes — runs locally
~96 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3090 Ti (24 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the FP16 quantization, which fits in 16.0 GB. Expected throughput is around 96 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 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an NVIDIA GeForce RTX 3090 Ti with FP16 quantization for Grade S performance at ~68 tok/sec.
Prerequisites
Before starting, ensure you have at least 20GB of free disk space, a 64-bit version of Windows 10/11 or Linux, the latest NVIDIA drivers (version 525.60.12 or later), and CUDA 11.7 installed.
Expected performance
With the recommended settings, you can expect ~68 tok/sec performance, using 16.0GB of VRAM, leaving 8.0GB of headroom for context. This allows for a practical context window of several hundred tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. Download the model
Download the FP16 quantized model (10.0GB file) from Hugging Face.
ollama pull Wan-AI/Wan2.2-TI2V-5B:Wan2.2-TI2V-5B.safetensors3. Run it
ollama run Wan2.2-TI2V-5B --n-gpu-layers 48 --flash-attn
ollama chat Wan2.2-TI2V-5B4. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the FP16 quantization and set --n-gpu-layers to 48. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 16.0GB VRAM in use, you will have 8.0GB of headroom for larger context windows.
Troubleshooting
Out of memory errors during inference
Reduce the number of layers on the GPU using --n-gpu-layers <N> where <N> is a lower number, e.g., --n-gpu-layers 32.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn and that the latest NVIDIA drivers and CUDA are installed.
Model fails to load
Verify that the model file has been downloaded correctly and that there are no network issues. Try re-downloading the model using the same command.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly GUI, llama.cpp for lightweight and portable execution, or Jan for advanced customization. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 3090 Ti.
Other models that run great on RTX 3090 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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