Can M3 Max run Qwen 2.5 Coder 14B?
Yes — runs locally
~36 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The M3 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 M3 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on an Apple M3 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 Ventura 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 Q8_0 quantization, you can expect the model to run at approximately 130 tokens per second, using around 15.1GB of VRAM. Given the 128GB VRAM on the Apple M3 Max, you will have about 112.9GB of headroom for context, allowing for a practical context window of up to 32768 tokens.
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 M3 Max
To optimize performance on the Apple M3 Max, ensure that the Metal/MLX backend is used to leverage the 128GB unified memory. This will allow the model to utilize the full 128GB VRAM, providing ample headroom for large context windows and efficient computation.
Troubleshooting
If you encounter issues with the Metal/MLX backend, try setting the environment variable `OLLAMA_BACKEND=metal`.
export OLLAMA_BACKEND=metal
If the model runs out of memory, reduce the batch size or context length.
ollama run Qwen2.5-Coder-14B-Instruct-Q8_0.gguf --context-length 16384
If the model is slow, ensure that the latest version of Ollama is installed.
brew upgrade ollama
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 more control over quantization, or MLX for direct Metal integration. Jan is another option but may not offer the same level of optimization for the Apple M3 Max.
Other models that run great on M3 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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