Can M4 Max run Phi-3.5 MoE?
Yes — runs locally
~26 tok/sec · Good — slight pause, then text streams smoothly.
The verdict
The M4 Max (128 GB VRAM) handles Phi-3.5 MoE comfortably using the Q4_K_M quantization, which fits in 24.1 GB. Expected throughput is around 26 tokens/second, which feels Good — slight pause, then text streams smoothly. in interactive use. Microsoft MoE — 16 experts of 3.8 B, 6.6 B active per token. Strong reasoning at modest cost.
Setup tutorial: Phi-3.5 MoE on M4 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-3.5 MoE runs exceptionally well on the Apple M4 Max with a Grade S performance, using the Q4_K_M quantization. Expect around 59 tokens per second with snappy responsiveness.
Prerequisites
Before starting, ensure you have at least 25GB 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 Q4_K_M quantization, you can expect a throughput of approximately 59 tokens per second, with 24.1GB of VRAM in use. Given the 128GB VRAM, you will have 103.9GB of headroom, allowing for a practical context window of up to 131,072 tokens, depending on the complexity of the input.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Phi-3.5 MoE model with Q4_K_M quantization, which is a 23.6GB file.
ollama pull bartowski/Phi-3.5-MoE-instruct-GGUF:Phi-3.5-MoE-instruct-Q4_K_M.gguf3. Run it
ollama run Phi-3.5-MoE-instruct-Q4_K_M.gguf
ollama chat4. Optimize for M4 Max
For optimal performance on the Apple M4 Max, utilize 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. The large VRAM allows for efficient handling of the 24.1GB VRAM requirement, leaving ample headroom for context and other tasks.
Troubleshooting
Low throughput or high latency
Ensure that the Metal/MLX backend is properly configured and that MPS layers are enabled. You can check this by running `ollama config` and verifying the settings.
Out of memory errors
Reduce the batch size or context length to fit within the available 128GB VRAM. You can adjust these settings in the Ollama configuration using `ollama config`.
Model fails to load
Verify that the model file is downloaded correctly and not corrupted. Re-run the `ollama pull` command to re-download the model.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also use alternatives like LM Studio, llama.cpp, or MLX. LM Studio provides a more graphical interface and is useful for users who prefer a GUI. llama.cpp is a lightweight option for command-line enthusiasts, and MLX offers additional flexibility for custom configurations. Choose the runtime based on your specific needs and preferences.
Other models that run great on M4 Max
FAQ (20)
What GPU do I need to run Phi-3.5 MoE?
To run Phi-3.5 MoE, you need a GPU with at least 24.1 GB of VRAM, such as an NVIDIA RTX 3090 or A6000.
Is Phi-3.5 MoE good for coding?
Phi-3.5 MoE is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 131,072 tokens.
Phi-3.5 MoE vs Llama 3.1 8B?
Phi-3.5 MoE has 41.9 billion parameters compared to Llama 3.1 8B's 8 billion, offering more sophisticated reasoning and context handling but requiring significantly more VRAM.
Can I run Phi-3.5 MoE on a Mac?
Yes, you can run Phi-3.5 MoE on a Mac with a compatible GPU that has at least 24.1 GB of VRAM, such as an eGPU setup.
How much VRAM does Phi-3.5 MoE need?
Phi-3.5 MoE requires 24.1 GB of VRAM, which is consistent across different quantization levels.
Is Phi-3.5 MoE censored?
Phi-3.5 MoE is not inherently censored, but its responses may be influenced by the training data and any filters applied during deployment.
Is Phi-3.5 MoE commercial-use allowed?
Yes, Phi-3.5 MoE is licensed under the MIT License, allowing for commercial use without additional restrictions.
Phi-3.5 MoE context length?
Phi-3.5 MoE has a context length of 131,072 tokens, which is significantly larger than many other models, enabling it to handle longer and more complex inputs.
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