Can RTX 3080 Ti run Qwen 2.5 Coder 14B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 3080 Ti (12 GB VRAM) handles Qwen 2.5 Coder 14B comfortably using the Q4_K_M quantization, which fits in 8.9 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — model too large for this GPU in interactive use. Powerful 14B code model. Excellent for complex programming tasks.
Setup tutorial: Qwen 2.5 Coder 14B on RTX 3080 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on an NVIDIA GeForce RTX 3080 Ti with Q4_K_M quantization for Grade A performance at ~48 tok/sec.
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 510.47.03 or later), and CUDA 11.2 or higher installed.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at approximately 48 tokens per second, using 8.9GB of VRAM. This leaves about 3.1GB of VRAM for context, enabling a practical context window of around 10,000 tokens.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. Download the model
Download the Qwen 2.5 Coder 14B Q4_K_M quantized model (8.4GB file) from Hugging Face.
ollama pull bartowski/Qwen2.5-Coder-14B-Instruct-GGUF:Qwen2.5-Coder-14B-Instruct-Q4_K_M.gguf3. Run it
ollama run Qwen2.5-Coder-14B-Instruct-Q4_K_M --n-gpu-layers 12 --flash-attn true4. Optimize for RTX 3080 Ti
For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use --n-gpu-layers 12 to offload some layers to CPU memory. Enable flash attention with --flash-attn true to reduce VRAM usage and improve speed. Given the 8.9GB VRAM requirement, you will have approximately 3.1GB of VRAM left for context, allowing for a practical context window of around 10,000 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers 8 or lower, or decrease the context window size.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn true and that the latest NVIDIA drivers and CUDA are installed.
Model fails to load
Verify that the model file has been downloaded correctly and that there is sufficient disk space. Try re-downloading the model.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. Use LM Studio for a more user-friendly GUI, and llama.cpp for more advanced customization options. Jan is another option but may require more manual configuration. For the NVIDIA GeForce RTX 3080 Ti, Ollama provides a good balance of ease of use and performance.
Other models that run great on RTX 3080 Ti
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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