Can RTX 5090 run Qwen 2.5 Coder 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 Coder 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. Powerful 14B code model. Excellent for complex programming tasks.
Setup tutorial: Qwen 2.5 Coder 14B on RTX 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on an NVIDIA GeForce RTX 5090 with Q8_0 quantization for Grade S performance at ~76 tok/sec.
Prerequisites
Before starting, ensure you have at least 15GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 525.60 or later, and CUDA 11.8 or later installed.
Expected performance
With the Q8_0 quantization, you can expect the model to run at approximately 76 tokens per second, using around 15.1GB of VRAM. This leaves about 16.9GB of VRAM for context, allowing for a practical context window of up to 32768 tokens, which is ideal for complex programming tasks.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Qwen 2.5 Coder 14B Q8_0 quantized model (14.6GB file) from Hugging Face.
ollama pull bartowski/Qwen2.5-Coder-14B-Instruct-GGUF:Qwen2.5-Coder-14B-Instruct-Q8_0.gguf3. Run it
ollama run Qwen2.5-Coder-14B-Instruct-Q8_0 --n-gpu-layers 14 --flash-attn --tensor-parallelism 2
ollama chat4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use --n-gpu-layers 14 to offload layers to the GPU, enable --flash-attn for faster attention computation, and set --tensor-parallelism 2 to utilize the full GPU capacity. This configuration ensures that the model runs efficiently within the 32GB VRAM limit.
Troubleshooting
Out of memory error during inference
Reduce the number of --n-gpu-layers or decrease --tensor-parallelism to 1 to lower VRAM usage.
Slow token generation speed
Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.
Model fails to load
Verify that the model file is downloaded correctly and that there is sufficient disk space available.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Each runtime has its own strengths, but Ollama provides a balanced approach for ease of use and performance on 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 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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