Can RTX 3090 Ti run Qwen 2.5 Coder 14B?
Yes — runs locally
~42 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 3090 Ti (24 GB VRAM) handles Qwen 2.5 Coder 14B comfortably using the Q8_0 quantization, which fits in 15.1 GB. Expected throughput is around 42 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Powerful 14B code model. Excellent for complex programming tasks.
Setup tutorial: Qwen 2.5 Coder 14B on RTX 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on a NVIDIA GeForce RTX 3090 Ti with Q8_0 quantization for Grade S performance at ~57 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 470.82.01 or later, and CUDA 11.4 or later installed.
Expected performance
With the Q8_0 quantization, you can expect the model to run at ~57 tok/sec with 15.1GB of VRAM in use. The remaining 8.9GB of VRAM provides ample headroom for a context window of up to 16,000 tokens, making it suitable 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.gguf --n-gpu-layers 32 --flash-attn --context-length 327684. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory. Enable --flash-attn for faster and more efficient attention calculations. Given the 15.1GB VRAM usage, you will have approximately 8.9GB of VRAM left for context, allowing for a practical context window of around 16,000 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of layers offloaded to the GPU using --n-gpu-layers or decrease the context length using --context-length.
Slow token generation speed
Ensure that --flash-attn is enabled and try increasing the batch size if applicable.
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
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is a good choice for a user-friendly interface, while llama.cpp offers more control over low-level optimizations. Jan is suitable for distributed inference across multiple GPUs. For the NVIDIA GeForce RTX 3090 Ti, Ollama provides a balanced approach with ease of use and performance.
Other models that run great on RTX 3090 Ti
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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