Can M4 Pro run Qwen 2.5 Coder 14B?
Yes — runs locally
~26 tok/sec · Good — slight pause, then text streams smoothly.
The verdict
The M4 Pro (48 GB VRAM) handles Qwen 2.5 Coder 14B comfortably using the Q8_0 quantization, which fits in 15.1 GB. Expected throughput is around 26 tokens/second, which feels Good — slight pause, then text streams smoothly. in interactive use. Powerful 14B code model. Excellent for complex programming tasks.
Setup tutorial: Qwen 2.5 Coder 14B on M4 Pro
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on an Apple M4 Pro with a Grade S performance, using the Q8_0 quantization. Expect ~49 tok/sec and snappy performance.
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, you can expect a token generation speed of ~49 tok/sec, utilizing 15.1GB of VRAM. Given the remaining 32.9GB of VRAM, you can achieve a practical context window of up to 32768 tokens, making it suitable for complex programming tasks.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Qwen 2.5 Coder 14B Instruct model with Q8_0 quantization (14.6GB file).
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
ollama chat --model Qwen2.5-Coder-14B-Instruct-Q8_04. Optimize for M4 Pro
For optimal performance on the Apple M4 Pro, leverage the Metal/MLX backend to utilize the 48GB of unified memory. Ensure that MPS layers are enabled to take full advantage of the GPU's capabilities. With 15.1GB of VRAM used by the model, you will have approximately 32.9GB of VRAM left for context, allowing for a large context window of up to 32768 tokens.
Troubleshooting
Model fails to load due to insufficient VRAM.
Ensure you have at least 48GB of VRAM available. If not, consider using a lower quantization like Q4_K_M.
Performance is slow or unresponsive.
Check if the Metal/MLX backend is enabled and MPS layers are utilized. Restart the runtime with `ollama restart`.
Error messages related to disk space.
Free up at least 15GB of disk space and try downloading the model again.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and MLX. LM Studio offers a more user-friendly interface but may not be as optimized for Apple Silicon. llama.cpp is highly customizable and can be fine-tuned for specific tasks, while MLX provides a low-level API for advanced users. For most users, Ollama is the preferred choice due to its ease of use and performance on Apple M4 Pro.
Other models that run great on M4 Pro
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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