Can M4 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 M4 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 M4 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 M4 Max with Q8_0 quantization, achieving ~607 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB 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 ~607 tok/sec with 4.3GB VRAM in use, leaving 123.7GB of VRAM headroom for context. This allows for a practical context window of up to 131072 tokens, given the remaining VRAM.

1. Install runtimeOllama (preferred on Apple Silicon)

brew install ollama
ollama init

2. Download the model

Download the Q8_0 quantized model (3.8GB file) from Hugging Face.

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 M4 Max

To optimize performance on the Apple M4 Max, leverage the Metal/MLX backend and MPS layers to utilize the 128GB unified memory efficiently. Ensure that the model is loaded into the unified memory to minimize data transfer latency and maximize throughput.

Troubleshooting

Low token generation speed

Ensure that the Metal/MLX backend is enabled and that the model is fully loaded into the unified memory. You can check this by running `ollama info`.

Out of memory errors

Reduce the batch size or context length to fit within the available VRAM. Adjust the `--context-length` parameter in the `ollama run` command.

Model not found

Verify that the model has been successfully downloaded and is located in the Ollama models directory. You can list all downloaded models using `ollama list`.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and MLX. LM Studio is suitable for a graphical interface, while llama.cpp offers more control over quantization and performance tuning. MLX is ideal for integrating the model into custom applications. For the Apple M4 Max, Ollama is generally the most straightforward and optimized option.

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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