Can M4 Max run Qwen 2.5 Coder 14B?
Yes — runs locally
~36 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The M4 Max (128 GB VRAM) handles Qwen 2.5 Coder 14B comfortably using the Q8_0 quantization, which fits in 15.1 GB. Expected throughput is around 36 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Powerful 14B code model. Excellent for complex programming tasks.
Setup tutorial: Qwen 2.5 Coder 14B on M4 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on an Apple M4 Max with Q8_0 quantization for Grade S performance at ~130 tok/sec.
Prerequisites
Before starting, ensure you have at least 15GB 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, expect ~130 tok/sec performance and 15.1GB VRAM usage. Given the 128GB VRAM, you have 112.9GB of headroom, allowing for a practical context window of up to 32768 tokens while maintaining smooth operation.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama setup2. Download the model
Download the Qwen 2.5 Coder 14B Q8_0 quantized model (14.6GB file) from Hugging Face.
ollama pull bartowski/Qwen2.5-Coder-14B-Instruct-GGUF:Qwen2.5-Coder-14B-Instruct-Q8_0.gguf3. Run it
ollama run Qwen2.5-Coder-14B-Instruct-Q8_0.gguf
ollama chat4. Optimize for M4 Max
For optimal performance on the Apple M4 Max, leverage the Metal/MLX backend to utilize the 128GB of unified memory. Ensure that MPS layers are enabled to take full advantage of the GPU's capabilities. With 128GB VRAM, you have ample headroom to handle large context windows and complex tasks.
Troubleshooting
Insufficient VRAM during model loading
Ensure you have at least 15GB of free disk space and 128GB of VRAM available. Close any unnecessary applications to free up resources.
Slow token generation speed
Check if the Metal/MLX backend is enabled and MPS layers are configured correctly. Run `ollama config --backend metal` to set the backend.
Model not found after pulling
Verify the model path and name. Run `ollama list` to check if the model was successfully downloaded and is available for use.
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 advanced customization, or MLX for direct Metal integration. Choose an alternative based on your specific needs, such as GUI preferences or fine-grained control over model execution.
Other models that run great on M4 Max
FAQ (20)
What GPU do I need to run Qwen 2.5 Coder 14B?
To run Qwen 2.5 Coder 14B, you need a GPU with at least 8.9 GB of VRAM, but 15.1 GB is recommended for optimal performance.
Is Qwen 2.5 Coder 14B good for coding?
Yes, Qwen 2.5 Coder 14B is excellent for complex programming tasks due to its large context length of 32,768 tokens and 14 billion parameters.
Qwen 2.5 Coder 14B vs Llama 3.1 8B?
Qwen 2.5 Coder 14B has more parameters (14B vs 8B) and a longer context length (32,768 vs typically shorter), making it better suited for complex coding tasks.
Can I run Qwen 2.5 Coder 14B on a Mac?
Yes, you can run Qwen 2.5 Coder 14B on a Mac, provided your Mac has a compatible GPU with sufficient VRAM (8.9 GB minimum, 15.1 GB recommended).
How much VRAM does Qwen 2.5 Coder 14B need?
Qwen 2.5 Coder 14B requires 8.9 GB to 15.1 GB of VRAM, depending on the quantization level used.
Is Qwen 2.5 Coder 14B censored?
Qwen 2.5 Coder 14B is not inherently censored, but it adheres to community guidelines and ethical standards in its responses.
Is Qwen 2.5 Coder 14B commercial-use allowed?
Yes, Qwen 2.5 Coder 14B is licensed under Apache-2.0, which allows for commercial use.
Qwen 2.5 Coder 14B context length?
Qwen 2.5 Coder 14B has a context length of 32,768 tokens, allowing it to handle very long sequences of text.
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