Can M4 Max run Wan 2.2 TI2V 5B?
Yes — runs locally
~74 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M4 Max (128 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the FP16 quantization, which fits in 16.0 GB. Expected throughput is around 74 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 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Wan 2.2 TI2V 5B on an Apple M4 Max with Grade S performance, using FP16 quantization for ~154 tok/sec.
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 a throughput of ~154 tok/sec, utilizing 16.0GB of VRAM. Given the 128GB total VRAM, you will have 112.0GB of headroom for larger context windows, allowing for more complex and longer video generations.
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-5B3. Run it
ollama run Wan2.2-TI2V-5B --device mps
ollama interactive Wan2.2-TI2V-5B4. Optimize for M4 Max
For optimal performance on the Apple M4 Max, use the Metal Performance Shaders (MPS) backend with the MLX runtime to leverage the 128GB unified memory. Ensure that the MPS layers are enabled in your environment settings to take full advantage of the GPU's capabilities.
Troubleshooting
Low performance or high latency
Ensure that the MPS backend is enabled and that you are using the latest version of Ollama. Run `ollama update` to check for updates.
Out of memory errors
Reduce the batch size or context length to fit within the available 16.0GB VRAM. Adjust the `--max_length` parameter in your run command.
Model not found
Verify that the model has been successfully downloaded and is available in your Ollama models directory. Run `ollama list` to check the available models.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio for a graphical interface, llama.cpp for more control over execution, or the MLX runtime for direct Metal integration. Use these alternatives if you need specific features or better integration with existing workflows.
Other models that run great on M4 Max
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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