Can RTX 4080 SUPER run Qwen 2.5 Coder 14B?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4080 SUPER (16 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 48 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 RTX 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on an NVIDIA GeForce RTX 4080 SUPER with Grade S performance, using the Q4_K_M quantization for ~64 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.12 or later), and CUDA 11.8 installed.
Expected performance
You can expect the model to run at approximately 64 tokens per second with 8.9GB of VRAM in use, leaving 7.1GB for context. This provides a practical context window of up to 20,000 tokens, suitable for complex programming tasks.
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.gguf --interactive
ollama chat --model Qwen2.5-Coder-14B-Instruct-Q4_K_M.gguf4. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 32 to balance between speed and memory usage. Enable flash attention with --flash-attn to improve efficiency. With 8.9GB VRAM used by the model, you have 7.1GB left for context, allowing for a practical context window of up to 20,000 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers 24 or lower.
Slow inference speed
Enable flash attention with --flash-attn and ensure CUDA is properly configured.
Model not found
Verify the model path and ensure it is correctly downloaded and accessible.
Alternative runtimes
Alternative runtimes include LM Studio for a more user-friendly interface, llama.cpp for low-level control, and Jan for specialized use cases. Use LM Studio for ease of use, llama.cpp for fine-grained optimization, and Jan for specific deployment scenarios. For the NVIDIA GeForce RTX 4080 SUPER, Ollama provides a good balance of performance and ease of use.
Other models that run great on RTX 4080 SUPER
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.
Want personalized recommendations for your exact setup? Detect my hardware →