Can RTX 4080 run Qwen 2.5 14B?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4080 (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 4080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 14B on an NVIDIA GeForce RTX 4080 with grade S performance, using the Q4_K_M quantization for ~64 tok/sec speed.
Prerequisites
Before starting, ensure you have at least 20GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA driver (version 525.60 or later), and CUDA 11.8 installed.
Expected performance
With the Q4_K_M quantization, you can expect ~64 tok/sec performance, utilizing 8.9GB of the 16GB VRAM. This leaves 7.1GB of VRAM for context, which is sufficient to handle large context windows, making it ideal for tasks requiring extensive reasoning and context.
1. Install runtimeOllama
curl -sSL https://ollama.ai/install.sh | sh
ollama install2. 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 --n-gpu-layers 32 --flash-attn --context-length 1310724. Optimize for RTX 4080
For optimal performance on the NVIDIA GeForce RTX 4080 with 16GB VRAM, use --n-gpu-layers 32 to offload some layers to the CPU, enabling flash attention (--flash-attn) to reduce memory usage and improve speed. With 8.9GB VRAM used by the model, you have 7.1GB of headroom for context, allowing for a practical context window of up to 131072 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers 16 or enable CPU offloading with --n-cpu-layers 32
Slow token generation
Ensure flash attention is enabled with --flash-attn and try reducing the context length if necessary
Model fails to load
Verify the model file integrity and reinstall Ollama with 'ollama install'
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for advanced customization, or Jan for lightweight deployment. Each has its own strengths, but Ollama provides a balanced approach for ease of use and performance on the RTX 4080.
Other models that run great on RTX 4080
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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