Can RTX 5070 Ti run Wan 2.2 TI2V 5B?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5070 Ti (16 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the Q8 quantization, which fits in 10.0 GB. Expected throughput is around 114 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 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an NVIDIA GeForce RTX 5070 Ti with Grade S performance at ~72 tok/sec using the Q8 quantization. This setup is optimized for 16GB VRAM.
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 525.60.11 or later), and CUDA 11.8 installed.
Expected performance
You can expect ~72 tok/sec performance with 10.0GB VRAM in use, leaving 6.0GB of headroom for context. This allows for a practical context window of several seconds of video, 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 --n-gpu-layers 16 --flash-attn
ollama chat Wan2.2-TI2V-5B-Q84. Optimize for RTX 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 16 to fully utilize the GPU. Enable --flash-attn for faster and more efficient attention computations. With 10.0GB VRAM used by the model, you have 6.0GB of headroom for larger context windows.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 8 or 12 to lower VRAM usage.
Slow inference speed
Ensure --flash-attn is enabled and check that your CUDA installation is up to date.
Model not loading
Verify the model file integrity and try re-downloading it using the 'ollama pull' command.
Alternative runtimes
Consider using LM Studio for a more user-friendly interface, llama.cpp for CPU-based inference, or Jan for distributed training. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 5070 Ti.
Other models that run great on RTX 5070 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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