Can RTX 4060 Ti 16GB run Qwen 2.5 Coder 14B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 4060 Ti 16GB (16 GB VRAM) handles Qwen 2.5 Coder 14B comfortably using the Q4_K_M quantization, which fits in 8.9 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — model too large for this GPU in interactive use. Powerful 14B code model. Excellent for complex programming tasks.
Setup tutorial: Qwen 2.5 Coder 14B on RTX 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on a NVIDIA GeForce RTX 4060 Ti 16GB with Grade S performance, using the Q4_K_M quantization for ~64 tok/sec.
Prerequisites
Before starting, ensure you have at least 20GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.8 installed.
Expected performance
With the Q4_K_M quantization, you can expect ~64 tok/sec with 8.9GB VRAM in use, leaving 7.1GB of VRAM for context. This allows for a practical context window of up to 32768 tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Qwen 2.5 Coder 14B Q4_K_M quantized model (8.4GB file) from Hugging Face.
ollama pull bartowski/Qwen2.5-Coder-14B-Instruct-GGUF:Qwen2.5-Coder-14B-Instruct-Q4_K_M.gguf3. Run it
ollama run Qwen2.5-Coder-14B-Instruct-Q4_K_M --n-gpu-layers 14 --flash-attn --tensor-parallelism 14. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 14 to fully utilize the 16GB VRAM. Enable --flash-attn for faster attention computations and set --tensor-parallelism to 1 to avoid splitting the model across multiple GPUs.
Troubleshooting
Out of memory error during inference
Reduce --n-gpu-layers to 12 or enable --cpu-offload to offload some layers to CPU.
Slow token generation speed
Ensure that --flash-attn is enabled and check if your CUDA installation is up-to-date.
Model fails to load
Verify that the model file was downloaded correctly and try re-downloading it.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly GUI, llama.cpp for fine-grained control over optimizations, or Jan for lightweight deployment. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 4060 Ti 16GB.
Other models that run great on RTX 4060 Ti 16GB
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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