Can M4 Max run Phi-3.5 Mini 3.8B?
Yes — runs locally
~74 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M4 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 M4 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-3.5 Mini 3.8B runs at Grade S on the Apple M4 Max with Q8_0 quantization, achieving ~609 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 the model to run at approximately 609 tokens per second, using around 4.3GB of VRAM. Given the 128GB of total VRAM, this leaves about 123.7GB of headroom for context, allowing for a practical context window of up to 131,072 tokens.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Q8_0 quantized model (3.8GB file) from the Hugging Face repository.
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 M4 Max
To optimize performance on the Apple M4 Max, use the Metal/MLX backend to leverage the 128GB of unified memory. Ensure that MPS layers are enabled to take full advantage of the GPU. The Q8_0 quantization is well-suited for the M4 Max, balancing speed and memory usage.
Troubleshooting
Low token generation speed
Ensure that the Metal/MLX backend is enabled and that MPS layers are utilized. You can check this by running `ollama config` and verifying the settings.
Out of memory errors
Reduce the batch size or context length. You can adjust these settings in the Ollama configuration using `ollama config set batch_size <value>` and `ollama config set context_length <value>`. For example, `ollama config set context_length 65536`.
Model not found
Verify that the model has been successfully downloaded and is listed in the Ollama models directory. You can list all available models by running `ollama models`.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio, llama.cpp, or MLX. LM Studio provides a graphical interface and is useful for users who prefer a GUI. llama.cpp is a lightweight option for systems with limited resources. MLX is another backend that can be used for fine-grained control over GPU operations. Jan is a newer runtime that may offer additional optimizations but is less tested on Apple Silicon.
Other models that run great on M4 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.
Want personalized recommendations for your exact setup? Detect my hardware →