Can RTX 3060 12GB run Wan 2.2 TI2V 5B?
Yes — runs locally
~58 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 3060 12GB (12 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the Q8 quantization, which fits in 10.0 GB. Expected throughput is around 58 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. 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 3060 12GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Wan 2.2 TI2V 5B runs at Grade A performance on an NVIDIA GeForce RTX 3060 12GB with the Q8 quantization, achieving ~54 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.7 or later installed.
Expected performance
With the Q8 quantization, you can expect ~54 tok/sec performance, using approximately 10.0GB of VRAM, leaving 2.0GB of headroom for context. This allows for a reasonable context window, suitable for generating 5-second 480p video clips.
1. Install runtimeOllama
pip install ollama
ollama init2. 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 --n-gpu-layers 12 --flash-attn
ollama chat Wan2.2-TI2V-5B-Q84. Optimize for RTX 3060 12GB
For optimal performance on the NVIDIA GeForce RTX 3060 12GB, set --n-gpu-layers to 12 to utilize the available 12GB VRAM effectively. Enable --flash-attn to speed up attention computations. Given the 12GB VRAM, you can achieve a practical context window that fits within the remaining VRAM after loading the model.
Troubleshooting
Out of memory error during model loading
Reduce --n-gpu-layers to 8 or 10 to lower VRAM usage.
Slow inference speed
Ensure --flash-attn is enabled and update your NVIDIA drivers to the latest version.
Model not found
Verify the model name and try pulling the model again using 'ollama pull Wan-AI/Wan2.2-TI2V-5B-Q8'.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for different scenarios. LM Studio is ideal for a graphical interface, llama.cpp offers more control over optimizations, and Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 3060 12GB.
Other models that run great on RTX 3060 12GB
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.
Want personalized recommendations for your exact setup? Detect my hardware →