Can RTX 4070 SUPER run Qwen 2.5 Coder 14B?
Yes — runs locally
~36 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4070 SUPER (12 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 36 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 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on an NVIDIA GeForce RTX 4070 SUPER with a Grade A performance of ~48 tok/sec using the Q4_K_M quantization. The model runs efficiently within the 12GB VRAM limit.
Prerequisites
Before starting, ensure you have at least 10GB 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 Q4_K_M quantization, you can expect a token generation rate of approximately 48 tokens per second, with 8.9GB of VRAM in use, leaving 3.1GB of VRAM for context. This allows for a practical context window of around 10,000 tokens, given the remaining VRAM.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. 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 32 --flash-attn
ollama chat Qwen2.5-Coder-14B-Instruct-Q4_K_M4. Optimize for RTX 4070 SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use the --n-gpu-layers 32 flag to offload some layers to the CPU, enabling efficient use of the 12GB VRAM. Additionally, enable flash attention (--flash-attn) to speed up inference and reduce memory usage. Tensor parallelism is not necessary for this model and GPU combination.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using --n-gpu-layers 16 or lower.
Slow token generation rate
Ensure flash attention is enabled with --flash-attn and that the CUDA runtime is configured correctly with ollama config set runtime cuda.
Model fails to load
Verify the model file integrity and try re-downloading it using the ollama pull command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more advanced features or better performance. LM Studio is ideal for a graphical interface, llama.cpp offers more control over quantization and optimization, and Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with good performance and ease of use for the NVIDIA GeForce RTX 4070 SUPER.
Other models that run great on RTX 4070 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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