Can RTX 5070 Ti run Qwen 2.5 Coder 14B?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 5070 Ti (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 48 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 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen 2.5 Coder 14B on a NVIDIA GeForce RTX 5070 Ti with grade S performance, using the Q4_K_M quantization for ~64 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 525.60.13 or later, and CUDA 11.8 or later installed.
Expected performance
With the Q4_K_M quantization, you can expect ~64 tok/sec performance with 8.9GB of 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 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 14 --flash-attn --tensor-parallelism 14. Optimize for RTX 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 14 to utilize most of the VRAM while leaving some headroom for context. Enable --flash-attn for faster inference and set --tensor-parallelism to 1 for single-GPU operation.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 12 or enable --cpu-offload to offload some layers to CPU.
Slow inference speed
Ensure --flash-attn is enabled and check that your CUDA installation is correct and up-to-date.
Model fails to load
Verify that the model file was downloaded correctly and that there is sufficient disk space available.
Alternative runtimes
For users who prefer different runtimes, consider LM Studio for a more user-friendly GUI, llama.cpp for lightweight and portable deployment, or Jan for advanced customization options. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 5070 Ti.
Other models that run great on RTX 5070 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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