Can RTX 5060 Ti run Wan 2.2 TI2V 5B?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5060 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 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an NVIDIA GeForce RTX 5060 Ti with Grade S performance at ~72 tok/sec using the Q8 quantization. Requires 10.0GB VRAM and 5.0GB disk space.
Prerequisites
Before starting, ensure you have at least 5.0GB of free disk space, a compatible OS (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 the model to run at ~72 tok/sec, consuming around 10.0GB of VRAM. The remaining 6.0GB of VRAM provides ample headroom for a practical context window, allowing for longer sequences and more complex tasks.
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 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention with --flash-attn, and set tensor parallelism to 1. This configuration will utilize approximately 10.0GB of VRAM, leaving 6.0GB for context and other operations, ensuring smooth and efficient model execution.
Troubleshooting
Out of memory (OOM) errors during inference
Reduce the number of GPU layers with --n-gpu-layers <num_layers> or decrease the batch size.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn and that the CUDA backend is properly configured.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
Alternative runtimes include LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, and Jan for advanced customization. Use LM Studio for ease of use, llama.cpp for minimal resource usage, and Jan for fine-grained control over the model's behavior.
Other models that run great on RTX 5060 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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