Can M3 Max run Phi-4 Mini 3.8B?

Yes — runs locally

~74 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

128 GB

Model size

3.8B

Best quant

Q8_0

VRAM needed

4.3 GB

The verdict

The M3 Max (128 GB VRAM) handles Phi-4 Mini 3.8B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 74 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Latest Phi mini with strong reasoning. Drop-in upgrade from Phi-3.5 Mini.

Setup tutorial: Phi-4 Mini 3.8B on M3 Max

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Phi-4 Mini 3.8B runs at Grade S on the Apple M3 Max with Q8_0 quantization, achieving ~607 tok/sec.

Prerequisites

Before starting, ensure you have at least 5GB of free disk space, macOS 13.0 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 Phi-4 Mini 3.8B to run at approximately 607 tokens per second, using 4.3GB of VRAM. Given the remaining 123.7GB of VRAM, you can achieve a practical context window of up to 131,072 tokens, making it highly efficient for large-scale reasoning tasks.

1. Install runtimeOllama (preferred on Apple Silicon)

brew install ollama
ollama init

2. Download the model

Download the Q8_0 quantized version of Phi-4 Mini 3.8B, which is 3.8GB in size.

ollama pull bartowski/microsoft_Phi-4-mini-instruct-GGUF:microsoft_Phi-4-mini-instruct-Q8_0.gguf

3. Run it

ollama run microsoft_Phi-4-mini-instruct-Q8_0.gguf
ollama chat

4. Optimize for M3 Max

For optimal performance on the Apple M3 Max, use the Metal/MLX backend to leverage the GPU's 128GB of unified memory. Ensure that MPS layers are enabled to take full advantage of the hardware. With 4.3GB of VRAM used by the model, you will have 123.7GB of headroom for context and other tasks.

Troubleshooting

If you encounter an 'out of memory' error, try reducing the batch size or context length.

ollama config set batch_size 16

If the model runs slowly, ensure that the Metal/MLX backend is enabled.

ollama config set backend metal

If you see an 'MPS not found' error, reinstall the Ollama runtime.

brew uninstall ollama && brew install ollama

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 more control over quantization, or MLX for direct Metal integration. Jan is another option for advanced users who need more customization. Choose based on your specific needs and preferences.

Full Phi-4 Mini 3.8B details →

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FAQ (20)

What GPU do I need to run Phi-4 Mini 3.8B?

To run Phi-4 Mini 3.8B, you need a GPU with at least 2.8 GB of VRAM, but 4.3 GB is recommended for optimal performance, especially with higher quantization levels.

Is Phi-4 Mini 3.8B good for coding?

Yes, Phi-4 Mini 3.8B is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 131,072 tokens, which allows it to handle complex code snippets and documentation.

Phi-4 Mini 3.8B vs Llama 3.1 8B?

Phi-4 Mini 3.8B has fewer parameters (3.8B vs 8B) but is more efficient in terms of VRAM usage and performance, making it a better choice for systems with limited resources. It also offers a larger context length of 131,072 tokens compared to Llama 3.1 8B.

Can I run Phi-4 Mini 3.8B on a Mac?

Yes, you can run Phi-4 Mini 3.8B on a Mac, provided your Mac has a compatible GPU with at least 2.8 GB of VRAM. Ensure you have the necessary drivers and software installed for optimal performance.

How much VRAM does Phi-4 Mini 3.8B need?

Phi-4 Mini 3.8B requires between 2.8 GB and 4.3 GB of VRAM, depending on the quantization level used. Higher quantization levels generally require more VRAM but offer better performance.

Is Phi-4 Mini 3.8B censored?

Phi-4 Mini 3.8B is not inherently censored, but it may include content filters or safeguards to prevent the generation of harmful or inappropriate content, as is common in many AI models.

Is Phi-4 Mini 3.8B commercial-use allowed?

Yes, Phi-4 Mini 3.8B is licensed under the MIT License, which allows for both personal and commercial use without additional restrictions.

Phi-4 Mini 3.8B context length?

Phi-4 Mini 3.8B has a context length of 131,072 tokens, which is significantly larger than many other models, allowing it to process and generate longer sequences of text.

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