Can RTX 5090 run Qwen 2.5 14B?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles Qwen 2.5 14B comfortably using the Q8_0 quantization, which fits in 15.1 GB. Expected throughput is around 78 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 14B on an NVIDIA GeForce RTX 5090 with Grade S performance at ~76 tok/sec using the Q8_0 quantization. Requires 15.1GB VRAM.
Prerequisites
Before starting, ensure you have at least 15GB 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 76 tokens per second with 15.1GB of VRAM in use, leaving 16.9GB of VRAM for context. This allows for a practical context window of up to 131072 tokens, making it suitable for long-form text generation and complex reasoning tasks.
1. Install runtimeOllama
pip install ollama
ollama init2. 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 --interactive
ollama chat Qwen2.5-14B-Instruct-Q8_0.gguf4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 40 to balance between speed and memory usage. Enable flash attention (--flash-attn) to reduce memory overhead and improve inference speed. With 32GB VRAM, you can achieve a practical context window of up to 131072 tokens while maintaining ~76 tok/sec.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers value to 30 or lower to free up more VRAM.
Slow inference speed
Ensure that flash attention is enabled with the --flash-attn flag and that your CUDA drivers are up to date.
Model fails to load
Verify that the model file was downloaded correctly and that the Ollama runtime is properly installed. Try reinstalling Ollama or pulling the model again.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for specific needs. LM Studio offers a user-friendly interface and is ideal for those who prefer a graphical environment. llama.cpp is highly optimized for low-memory systems but may require more manual configuration. Jan is lightweight and easy to set up, making it a good choice for quick prototyping. However, Ollama provides a balanced combination of ease of use and performance, making it the recommended choice for the NVIDIA GeForce RTX 5090.
Other models that run great on RTX 5090
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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