Can RTX 5080 run Wan 2.2 TI2V 5B?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5080 (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 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an NVIDIA GeForce RTX 5080 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 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.8 installed.
Expected performance
You can expect the model to run at approximately 72 tokens per second with 10.0GB of VRAM in use, leaving 6.0GB of VRAM for context. This should allow for a practical context window of several seconds, suitable for generating 5-second 480p video clips.
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 interactive Wan2.2-TI2V-5B-Q84. Optimize for RTX 5080
For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 50 to utilize the GPU efficiently. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 10.0GB VRAM used by the model, you have 6.0GB of headroom for context, allowing for a practical context window of several seconds.
Troubleshooting
Out of memory errors during inference.
Reduce the --n-gpu-layers value or decrease the batch size.
Slow inference times.
Ensure flash attention is enabled with --flash-attn and check that CUDA is properly installed and configured.
Model fails to load.
Verify the integrity of the downloaded model file and try re-downloading it using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes include LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, and Jan for advanced customization options. Use LM Studio for ease of use, llama.cpp for minimal system requirements, and Jan for fine-grained control over model parameters. However, Ollama provides a balanced approach with good performance and ease of use on the NVIDIA GeForce RTX 5080.
Other models that run great on RTX 5080
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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