Can M4 Max run Phi-4?
Yes — runs locally
~36 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The M4 Max (128 GB VRAM) handles Phi-4 comfortably using the Q8_0 quantization, which fits in 15.0 GB. Expected throughput is around 36 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Microsoft's 14B parameter model. Punches well above its weight on reasoning.
Setup tutorial: Phi-4 on M4 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-4 runs at Grade S on the Apple M4 Max with Q8_0 quantization, achieving ~130 tok/sec, making it an excellent choice for high-performance inference.
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 the terminal.
Expected performance
With the Q8_0 quantization, you can expect Phi-4 to run at approximately 130 tokens per second, using around 15.0GB of VRAM. This leaves you with 113.0GB of VRAM headroom, allowing for a practical context window of up to 16384 tokens without running into memory constraints.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Q8_0 quantized Phi-4 model (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 Max
For optimal performance on the Apple M4 Max, use the Metal/MLX backend to leverage the 128GB of unified memory. Ensure that MPS layers are enabled to take full advantage of the GPU's capabilities. The Q8_0 quantization is well-suited for the 128GB VRAM, providing a balance between speed and memory usage.
Troubleshooting
Low performance or high latency
Ensure that the Metal/MLX backend is enabled and that MPS layers are utilized. Run `ollama config set backend metal` to set the backend.
Out of memory errors
Reduce the batch size or context length to fit within the available VRAM. For example, try `ollama run phi-4-Q8_0 --context-length 8192`.
Model not found
Verify that the model was successfully downloaded and is available in the 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 consider LM Studio for a more user-friendly interface, llama.cpp for custom builds, MLX for direct Metal integration, and Jan for lightweight deployment. Choose an alternative based on your specific needs, such as ease of use, customization, or resource efficiency.
Other models that run great on M4 Max
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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