Can M3 Max run Phi-3.5 Mini 3.8B?
Yes — runs locally
~74 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M3 Max (128 GB VRAM) handles Phi-3.5 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. Tiny but capable 3.8B model. Runs on almost any hardware including phones.
Setup tutorial: Phi-3.5 Mini 3.8B on M3 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Phi-3.5 Mini 3.8B model runs at Grade S on the Apple M3 Max with Q8_0 quantization, achieving ~609 tok/sec.
Prerequisites
Before starting, ensure you have at least 5GB of free disk space, macOS 12 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 the model to run at ~609 tok/sec, using approximately 4.3GB of VRAM. Given the 128GB of VRAM on the Apple M3 Max, you will have 123.7GB of headroom for context, allowing for a practical context window of up to 131072 tokens.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Q8_0 quantized version of Phi-3.5 Mini 3.8B, which is 3.8GB in size.
ollama pull bartowski/Phi-3.5-mini-instruct-GGUF:Phi-3.5-mini-instruct-Q8_0.gguf3. Run it
ollama run Phi-3.5-mini-instruct-Q8_0.gguf
ollama chat --model Phi-3.5-mini-instruct-Q8_0.gguf4. 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 GPU's capabilities. The large amount of VRAM allows for efficient handling of the 4.3GB VRAM requirement of the Q8_0 quantization.
Troubleshooting
Low token generation speed
Ensure that the Metal/MLX backend is enabled and that MPS layers are utilized. You can check and enable these settings in the Ollama configuration.
Out of memory errors
Reduce the batch size or context length to fit within the available VRAM. Adjust the context length to a lower value if necessary.
Model not loading
Verify that the model file has been downloaded correctly and that the path specified in the `ollama run` command is correct.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio, llama.cpp, or MLX for more advanced configurations. LM Studio is useful for a graphical interface, llama.cpp offers more control over quantization and performance tuning, and MLX is ideal for integrating the model into custom applications. Choose an alternative based on your specific needs and preferences.
Other models that run great on M3 Max
FAQ (20)
What GPU do I need to run Phi-3.5 Mini 3.8B?
Phi-3.5 Mini 3.8B requires a GPU with at least 2.7 GB of VRAM, but 4.3 GB is recommended for optimal performance.
Is Phi-3.5 Mini 3.8B good for coding?
Phi-3.5 Mini 3.8B is capable of generating code and providing coding assistance, but its performance is best suited for simpler tasks due to its 3.8B parameters.
Phi-3.5 Mini 3.8B vs Llama 3.1 8B?
Phi-3.5 Mini 3.8B has 3.8B parameters, making it smaller and more resource-efficient than Llama 3.1 8B, which has 8B parameters and requires more VRAM and computational power.
Can I run Phi-3.5 Mini 3.8B on a Mac?
Yes, Phi-3.5 Mini 3.8B can run on a Mac, provided your Mac has a compatible GPU with at least 2.7 GB of VRAM.
How much VRAM does Phi-3.5 Mini 3.8B need?
Phi-3.5 Mini 3.8B requires a minimum of 2.7 GB of VRAM, but 4.3 GB is recommended for better performance, depending on the quantization level.
Is Phi-3.5 Mini 3.8B censored?
Phi-3.5 Mini 3.8B is not inherently censored, but it may include content filters to prevent harmful or inappropriate content.
Is Phi-3.5 Mini 3.8B commercial-use allowed?
Yes, Phi-3.5 Mini 3.8B is licensed under the MIT License, which allows for commercial use.
Phi-3.5 Mini 3.8B context length?
Phi-3.5 Mini 3.8B supports a context length of 131,072 tokens, which is quite large and allows for extensive context in conversations and tasks.
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