Can RTX 4070 Ti SUPER run Qwen 2.5 Coder 14B?
Yes — runs locally
~42 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4070 Ti SUPER (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 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 4070 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on an NVIDIA GeForce RTX 4070 Ti SUPER with Grade S performance, using the Q4_K_M quantization for snappy ~64 tok/sec speed.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.8 or later installed.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at approximately 64 tokens per second, using around 8.9GB of VRAM. Given the remaining 7.1GB of VRAM, you can achieve a practical context window of up to 16,384 tokens, which is suitable for most complex programming tasks.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Qwen 2.5 Coder 14B model with Q4_K_M quantization (8.4GB file size) 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 32 --flash-attn
ollama chat Qwen2.5-Coder-14B-Instruct-Q4_K_M4. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, use the --n-gpu-layers 32 flag to offload some layers to the CPU, enabling flash attention (--flash-attn) to reduce memory usage. This configuration ensures that the model runs efficiently within the 16GB VRAM limit, leaving about 7.1GB of VRAM for context.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers 16 or disable flash attention with --no-flash-attn.
Slow inference speed
Ensure that the CUDA toolkit is correctly installed and that the NVIDIA drivers are up to date. Also, try increasing the batch size with --batch-size 16.
Model fails to load
Verify that the model file was downloaded correctly and that there are no disk space issues. Re-run the download command if necessary.
Alternative runtimes
While Ollama is recommended for its ease of use and performance, you can also consider LM Studio for a more user-friendly interface, llama.cpp for advanced customization options, or Jan for lightweight deployment. Choose an alternative runtime based on your specific needs, such as ease of use, customization, or resource efficiency.
Other models that run great on RTX 4070 Ti SUPER
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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