Can RTX 4070 Ti SUPER run Wan 2.2 TI2V 5B?
Yes — runs locally
~102 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 Ti 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 102 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 4070 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an NVIDIA GeForce RTX 4070 Ti SUPER with Q8 quantization for Grade S performance at ~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 the model to run at ~72 tok/sec, consuming around 10.0GB of VRAM. This leaves 6.0GB of VRAM for context, allowing for a practical context window of several seconds of video content.
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 --device cuda
ollama chat Wan2.2-TI2V-5B-Q84. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 50 and enable flash attention using --flash-attn. This configuration will utilize approximately 10.0GB of VRAM, leaving 6.0GB for context and other operations. Tensor parallelism is not necessary for this model on this GPU.
Troubleshooting
Out of memory error during inference
Reduce the number of layers on the GPU using --n-gpu-layers 40 or lower.
Slow token generation speed
Ensure flash attention is enabled with --flash-attn and check your CUDA installation.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. LM Studio is a good choice for a more user-friendly interface, while llama.cpp offers more control over low-level optimizations. Jan is another option for those who prefer a web-based interface. Choose based on your specific needs and comfort level with command-line tools.
Other models that run great on RTX 4070 Ti 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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