Can RTX 5080 run Qwen 2.5 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 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. Strong 14B model with excellent coding and reasoning. iPad Pro recommended.
Setup tutorial: Qwen 2.5 14B on RTX 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 14B on your NVIDIA GeForce RTX 5080 with Grade S performance, using the Q4_K_M quantization. Expect ~64 tok/sec and snappy interactions.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60.13 or later) with 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. The remaining 7.1GB of VRAM provides ample headroom for a large context window, enabling you to handle extensive conversations or complex tasks.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Qwen 2.5 14B Q4_K_M quantized model (8.4GB file) from Hugging Face.
ollama pull bartowski/Qwen2.5-14B-Instruct-GGUF:Qwen2.5-14B-Instruct-Q4_K_M.gguf3. Run it
ollama run Qwen2.5-14B-Instruct-Q4_K_M --interactive
ollama chat Qwen2.5-14B-Instruct-Q4_K_M4. Optimize for RTX 5080
For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 40 to fully utilize the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 8.9GB VRAM used by the model, you have 7.1GB left for context, allowing for a practical context window of up to 131,072 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 30.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn. If the issue persists, try reducing the batch size.
Model fails to load
Verify that the model file has been downloaded correctly and is not corrupted. Re-run the download command if necessary.
Alternative runtimes
If you prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, or Jan for a lightweight, portable solution. Ollama is recommended for its ease of use and robust performance on the NVIDIA GeForce RTX 5080.
Other models that run great on RTX 5080
FAQ (20)
What GPU do I need to run Qwen 2.5 14B?
To run Qwen 2.5 14B, you need a GPU with at least 8.9 GB of VRAM, but 15.1 GB is recommended for optimal performance, especially for larger context lengths and higher precision.
Is Qwen 2.5 14B good for coding?
Yes, Qwen 2.5 14B is excellent for coding tasks, offering strong performance in generating code, understanding complex programming concepts, and providing detailed explanations.
Qwen 2.5 14B vs Llama 3.1 8B?
Qwen 2.5 14B has more parameters (14B vs 8B), which generally results in better performance in complex tasks like coding and reasoning, but requires more VRAM and computational resources.
Can I run Qwen 2.5 14B on a Mac?
Yes, you can run Qwen 2.5 14B on a Mac, but ensure your Mac has a compatible GPU with sufficient VRAM. M1/M2 chips with Metal support can also run the model efficiently.
How much VRAM does Qwen 2.5 14B need?
Qwen 2.5 14B requires between 8.9 GB and 15.1 GB of VRAM, depending on the quantization level used. Higher quantization levels reduce VRAM usage but may slightly impact performance.
Is Qwen 2.5 14B censored?
Qwen 2.5 14B is not inherently censored, but it adheres to ethical guidelines and content policies to ensure responsible use and avoid harmful or inappropriate content.
Is Qwen 2.5 14B commercial-use allowed?
Yes, Qwen 2.5 14B is licensed under the Apache-2.0 license, which allows commercial use as long as you comply with the terms of the license.
Qwen 2.5 14B context length?
Qwen 2.5 14B supports a context length of up to 131,072 tokens, making it suitable for handling very long documents and conversations.
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