Can M4 Pro run Phi-4?
Yes — runs locally
~26 tok/sec · Good — slight pause, then text streams smoothly.
The verdict
The M4 Pro (48 GB VRAM) handles Phi-4 comfortably using the Q8_0 quantization, which fits in 15.0 GB. Expected throughput is around 26 tokens/second, which feels Good — slight pause, then text streams smoothly. in interactive use. Microsoft's 14B parameter model. Punches well above its weight on reasoning.
Setup tutorial: Phi-4 on M4 Pro
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-4 runs exceptionally well on the Apple M4 Pro with a Grade S performance, using the Q8_0 quantization. Expect ~49 tokens per second with snappy responsiveness.
Prerequisites
Before starting, ensure you have at least 15GB 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 Q8_0 quantization, you can expect a performance of ~49 tokens per second, with 15.0GB of VRAM in use. Given the remaining 33.0GB of VRAM, you can achieve a practical context window of up to 16384 tokens, ensuring smooth and efficient reasoning tasks.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama setup2. Download the model
Download the Phi-4 model with Q8_0 quantization (14.5GB file) from the Hugging Face repository.
ollama pull bartowski/phi-4-GGUF:phi-4-Q8_0.gguf3. Run it
ollama run phi-4-Q8_0
ollama chat --model phi-4-Q8_04. Optimize for M4 Pro
For optimal performance on the Apple M4 Pro, utilize the Metal/MLX backend to leverage the 48GB of unified memory. Ensure that MPS layers are enabled to take full advantage of the GPU's capabilities. With 15.0GB VRAM used by the model, you will have 33.0GB of VRAM remaining, which is ample for handling large context windows.
Troubleshooting
Model fails to load due to insufficient VRAM
Ensure that the Metal/MLX backend is properly configured and that you have at least 48GB of unified memory available. Try restarting your machine to clear any unused memory.
Slow token generation speed
Check if the MPS layers are enabled. You can enable them by setting the environment variable `MPS_ENABLE=1` before running the model.
Ollama installation fails
Ensure Homebrew is up to date by running `brew update`. If the issue persists, try installing Ollama manually from the official GitHub repository.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio for a more graphical interface, llama.cpp for command-line flexibility, or MLX for custom model deployment. Jan is another option but may require additional configuration for optimal performance on the Apple M4 Pro.
Other models that run great on M4 Pro
FAQ (20)
What GPU do I need to run Phi-4?
To run Phi-4, you need a GPU with at least 8.9 GB of VRAM, but 15.0 GB is recommended for optimal performance.
Is Phi-4 good for coding?
Yes, Phi-4 is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 16,384 tokens.
Phi-4 vs Llama 3.1 8B?
Phi-4 has 14 billion parameters compared to Llama 3.1's 8 billion, making it more powerful for complex tasks but requiring more VRAM.
Can I run Phi-4 on a Mac?
Yes, you can run Phi-4 on a Mac with a compatible GPU, such as an AMD or NVIDIA card with sufficient VRAM.
How much VRAM does Phi-4 need?
Phi-4 requires between 8.9 GB and 15.0 GB of VRAM, depending on the quantization level used.
Is Phi-4 censored?
Phi-4 is not inherently censored, but its outputs can be filtered based on the implementation and configuration settings.
Is Phi-4 commercial-use allowed?
Yes, Phi-4 is licensed under the MIT License, which allows for commercial use without restriction.
Phi-4 context length?
Phi-4 has a context length of 16,384 tokens, allowing it to handle longer sequences of text effectively.
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