Can M3 Max run Phi-4?
Yes — runs locally
~36 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The M3 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 M3 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-4 runs at Grade S on the Apple M3 Max with Q8_0 quantization, achieving ~130 tok/sec. This setup leverages the 128GB VRAM for optimal performance.
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. Given the 128GB VRAM, you will have 113.0GB of headroom for context, allowing for a practical context window of up to 16384 tokens.
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 M3 Max
For optimal performance on the Apple M3 Max, enable the Metal Performance Shaders (MPS) layers and utilize the unified memory architecture. The 128GB VRAM allows for efficient handling of large models like Phi-4, ensuring minimal memory bottlenecks and high throughput.
Troubleshooting
Low token generation speed
Ensure that the Metal Performance Shaders (MPS) are enabled and that the unified memory is being utilized effectively. Run `ollama config set use_mps true`.
Out of memory errors
Reduce the batch size or context window to fit within the available VRAM. Adjust the context length using `ollama config set max_context_length <value>`.
Model not found
Verify that the model was successfully downloaded and is available in the Ollama model 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, llama.cpp, or MLX for more advanced customization. LM Studio is ideal for GUI-based interaction, llama.cpp offers fine-grained control over inference parameters, and MLX is suitable for integrating the model into larger machine learning pipelines. Jan is another lightweight option for quick prototyping.
Other models that run great on M3 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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