Can RTX 4070 SUPER run Wan 2.2 TI2V 5B?
Yes — runs locally
~94 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 SUPER (12 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the Q8 quantization, which fits in 10.0 GB. Expected throughput is around 94 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 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an NVIDIA GeForce RTX 4070 SUPER with Grade A performance at ~54 tok/sec using the Q8 quantization. Requires 10.0GB VRAM.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8.
Expected performance
With the Q8 quantization, you can expect ~54 tok/sec performance, utilizing approximately 10.0GB of VRAM, leaving 2.0GB 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 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 SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use the --n-gpu-layers parameter to offload some layers to the CPU if necessary. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. Given the 12GB VRAM, you can set --n-gpu-layers to 40 to balance between performance and memory usage.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using --n-gpu-layers 30 or lower.
Slow inference speed
Enable flash attention with --flash-attn and ensure CUDA is properly installed and up-to-date.
Model fails to load
Check the integrity of the downloaded model file and try re-downloading it using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for a more graphical interface, while llama.cpp offers more control over optimizations and is ideal for advanced users. Jan is a lightweight alternative that may be preferred for simpler setups or environments with limited resources.
Other models that run great on RTX 4070 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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