Can RTX 5080 run Qwen 2.5 Coder 14B?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 5080 (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 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on an NVIDIA GeForce RTX 5080 with Grade S performance at ~64 tok/sec using the Q4_K_M quantization. Requires 8.9GB VRAM.
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 or later), and CUDA 11.8 or later installed.
Expected performance
With the Q4_K_M quantization, you should expect a token generation rate of ~64 tok/sec, with 8.9GB of VRAM in use. This leaves approximately 7.1GB of VRAM for context, allowing for a practical context window of up to 16384 tokens.
1. Install runtimeOllama
pip install ollama
ollama config set cuda_path /usr/local/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 5080
For optimal performance on the NVIDIA GeForce RTX 5080 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. Given the 16GB VRAM, you can achieve a practical context window of up to 16384 tokens while maintaining ~64 tok/sec.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers with --n-gpu-layers or decrease the context length.
Slow token generation
Ensure that flash attention is enabled with --flash-attn and that the CUDA path is correctly set in Ollama.
Model fails to load
Verify that the model file is fully downloaded and not corrupted. Try re-downloading the model file.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. LM Studio is suitable for users who prefer a graphical interface and need more advanced features like model fine-tuning. llama.cpp is ideal for those who require more control over the command line and want to experiment with different optimizations. For the NVIDIA GeForce RTX 5080, Ollama provides a well-balanced solution for ease of use and performance.
Other models that run great on RTX 5080
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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