Can RTX 4060 Ti 16GB run Wan 2.2 TI2V 5B?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4060 Ti 16GB (16 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the Q8 quantization, which fits in 10.0 GB. Expected throughput is around 78 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 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an NVIDIA GeForce RTX 4060 Ti 16GB with Grade S performance, using the Q8 quantization for optimal speed (~72 tok/sec) and memory efficiency.
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 525.60.13 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. The model will use around 10.0GB of VRAM, leaving 6.0GB of headroom for context, allowing for a practical context window of several hundred tokens.
1. Install runtimeOllama
pip install ollama
ollama config set device=cuda2. Download the model
Download the Q8 quantized version of the model, which is a 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 --n-gpu-layers=32 --flash-attn
ollama chat --model=Wan2.2-TI2V-5B-Q84. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 32 to utilize the full 16GB VRAM efficiently. Enable --flash-attn to reduce memory usage and improve speed. With 10.0GB VRAM in use, you will have 6.0GB of headroom for larger context windows.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 24 or enable --cpu-offload to offload some layers to the CPU.
Slow token generation
Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.
Model fails to load
Check that the model file is fully downloaded and not corrupted. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes include LM Studio for a more user-friendly GUI, llama.cpp for lightweight deployment, and Jan for advanced customization options. Use these alternatives if you need specific features not available in Ollama, such as custom model modifications or integration with other tools.
Other models that run great on RTX 4060 Ti 16GB
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 →