Can M4 Pro run Wan 2.2 TI2V 5B?
Yes — runs locally
~62 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M4 Pro (48 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the FP16 quantization, which fits in 16.0 GB. Expected throughput is around 62 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 M4 Pro
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an Apple M4 Pro with Grade S performance at ~58 tok/sec using the FP16 quantization. Requires 16.0GB VRAM, leaving 32.0GB headroom.
Prerequisites
Before starting, ensure you have at least 20GB of free disk space, macOS 12.3 or later, and Xcode Command Line Tools installed. You can install Xcode CLT by running `xcode-select --install` in your terminal.
Expected performance
With the FP16 quantization, you should expect ~58 tok/sec performance, utilizing 16.0GB of VRAM. Given the 32.0GB headroom, you can achieve a practical context window of several thousand tokens, depending on the complexity of the generated content.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama setup2. Download the model
Download the FP16 quantized model (10.0GB file) from Hugging Face.
ollama pull Wan-AI/Wan2.2-TI2V-5B:fp163. Run it
ollama run Wan2.2-TI2V-5B --device metal
ollama interactive Wan2.2-TI2V-5B4. Optimize for M4 Pro
For optimal performance on the Apple M4 Pro, use the Metal/MLX backend to leverage the 48GB unified memory. Ensure that MPS layers are enabled to take full advantage of the GPU. With 16.0GB VRAM in use, you have 32.0GB of headroom for larger context windows and more complex tasks.
Troubleshooting
Low performance or out-of-memory errors
Ensure you are using the Metal/MLX backend and that MPS layers are enabled. Adjust the batch size if necessary.
Model not found
Verify the model name and ensure it is correctly downloaded using `ollama list`.
Slow startup times
Preload the model into memory using `ollama preload Wan2.2-TI2V-5B`.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and MLX. Use LM Studio for a graphical interface, llama.cpp for lightweight deployment, and MLX for direct Metal integration. Ollama is generally preferred for its ease of use and performance on Apple Silicon.
Other models that run great on M4 Pro
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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