Can RTX 4090 run Qwen 2.5 Coder 14B?
Yes — runs locally
~66 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (24 GB VRAM) handles Qwen 2.5 Coder 14B comfortably using the Q8_0 quantization, which fits in 15.1 GB. Expected throughput is around 66 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Powerful 14B code model. Excellent for complex programming tasks.
Setup tutorial: Qwen 2.5 Coder 14B on RTX 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on an NVIDIA GeForce RTX 4090 with Grade S performance, using the Q8_0 quantization for ~57 tok/sec speed.
Prerequisites
Before starting, ensure you have at least 15GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.8 or later installed.
Expected performance
With the Q8_0 quantization, you can expect the model to run at approximately 57 tokens per second, using 15.1GB of VRAM. The remaining 8.9GB of VRAM provides ample headroom for handling large context windows, making it suitable for complex programming tasks.
1. Install runtimeOllama
pip install ollama
ollama init2. 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 --n-gpu-layers 56 --flash-attn --tensor-parallelism 14. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, set --n-gpu-layers to 56 to utilize most of the GPU's memory. Enable --flash-attn for faster and more efficient attention calculations. Given the 15.1GB VRAM requirement, you will have approximately 8.9GB of VRAM left for context, allowing for a practical context window of up to 32768 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of --n-gpu-layers or increase the batch size to fit within the available VRAM.
Slow inference speed
Ensure that --flash-attn is enabled and that the CUDA toolkit is correctly installed and up to date.
Model not loading
Verify that the model file has been downloaded correctly and that the Ollama runtime is properly installed and configured.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a user-friendly interface and is ideal for those who prefer a graphical environment. llama.cpp is a lightweight option for running models directly from the command line, suitable for advanced users. Jan is another lightweight runtime that supports a wide range of models and is good for experimentation. Choose the runtime based on your specific needs and comfort level with command-line tools.
Other models that run great on RTX 4090
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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