Can RTX 4090 run Qwen 2.5 14B?
Yes — runs locally
~66 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (24 GB VRAM) handles Qwen 2.5 14B comfortably using the Q8_0 quantization, which fits in 15.1 GB. Expected throughput is around 66 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Strong 14B model with excellent coding and reasoning. iPad Pro recommended.
Setup tutorial: Qwen 2.5 14B on RTX 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 14B on an NVIDIA GeForce RTX 4090 with Grade S performance, using the Q8_0 quantization for ~57 tok/sec speed.
Prerequisites
Before starting, ensure you have at least 15GB of free disk space, a compatible operating system (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 57 tokens per second with 15.1GB of VRAM in use, leaving 8.9GB of VRAM for context. This provides ample headroom to handle large context windows, making it suitable for tasks requiring extensive context.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. Download the model
Download the Qwen 2.5 14B Q8_0 quantized model (14.6GB file) from Hugging Face.
ollama pull bartowski/Qwen2.5-14B-Instruct-GGUF:Qwen2.5-14B-Instruct-Q8_0.gguf3. Run it
ollama run Qwen2.5-14B-Instruct-Q8_0.gguf --n-gpu-layers 40 --flash-attn
ollama chat Qwen2.5-14B-Instruct-Q8_0.gguf4. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, set --n-gpu-layers to 40 to utilize the GPU efficiently. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 15.1GB VRAM used by the model, you will have 8.9GB of VRAM left for context, allowing for a practical context window of up to 131072 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of layers offloaded to the GPU by decreasing --n-gpu-layers or increase the batch size to better utilize the available VRAM.
Slow inference speed
Ensure that flash attention is enabled (--flash-attn) 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 are no issues with the file path. Try re-downloading the model file.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is a good choice for a more user-friendly interface, while llama.cpp offers more control over low-level optimizations. Jan is suitable for users who prefer a lightweight, command-line-based solution. For the NVIDIA GeForce RTX 4090, Ollama is recommended due to its ease of use and efficient GPU utilization.
Other models that run great on RTX 4090
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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