Can RTX 5090 run Wan 2.2 TI2V 5B?
Yes — runs locally
~168 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the FP16 quantization, which fits in 16.0 GB. Expected throughput is around 168 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
The NVIDIA GeForce RTX 5090 runs the Wan 2.2 TI2V 5B model at Grade S performance, using the FP16 quantization, achieving ~90 tok/sec.
Prerequisites
Before starting, ensure you have at least 10.0GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.
Expected performance
With the FP16 quantization, you can expect the model to achieve ~90 tok/sec, utilizing 16.0GB of VRAM. Given the remaining 16.0GB of VRAM, you can achieve a practical context window of several thousand tokens, suitable for generating high-quality 5-second 480p video clips.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. Download the model
Download the FP16 quantized model (10.0GB file) from Hugging Face.
ollama pull Wan-AI/Wan2.2-TI2V-5B:Wan2.2-TI2V-5B.safetensors3. Run it
ollama run Wan2.2-TI2V-5B --device cuda --dtype fp16
ollama chat Wan2.2-TI2V-5B4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use the FP16 quantization. Set --n-gpu-layers to 50 to maximize GPU utilization. Enable flash-attn to reduce memory usage and improve speed. With 16.0GB VRAM used by the model, you have 16.0GB of headroom for context, allowing for longer sequences.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers parameter or enable flash-attn.
Low token generation speed
Ensure CUDA is properly installed and the device is set to cuda in Ollama.
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 offers a more user-friendly interface and is suitable for users who prefer a graphical environment. llama.cpp provides more fine-grained control over model execution and is ideal for advanced users or those requiring specific optimizations. For the NVIDIA GeForce RTX 5090, Ollama is generally the best choice due to its ease of use and strong performance out-of-the-box.
Other models that run great on RTX 5090
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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