Can RTX 5060 Ti run Qwen 2.5 14B?

Yes — runs locally

~48 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

16 GB

Model size

14B

Best quant

Q4_K_M

VRAM needed

8.9 GB

The verdict

The RTX 5060 Ti (16 GB VRAM) handles Qwen 2.5 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. Strong 14B model with excellent coding and reasoning. iPad Pro recommended.

Setup tutorial: Qwen 2.5 14B on RTX 5060 Ti

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Qwen 2.5 14B runs Grade S on the NVIDIA GeForce RTX 5060 Ti with Q4_K_M quantization, achieving ~64 tok/sec.

Prerequisites

Before starting, ensure you have at least 20GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with 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 131072 tokens, depending on the complexity of the input.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Qwen 2.5 14B Q4_K_M quantized model (8.4GB file) from Hugging Face.

ollama pull bartowski/Qwen2.5-14B-Instruct-GGUF:Qwen2.5-14B-Instruct-Q4_K_M.gguf

3. Run it

ollama run Qwen2.5-14B-Instruct-Q4_K_M --n-gpu-layers 14 --flash-attn --tensor-parallelism 1

4. Optimize for RTX 5060 Ti

For optimal performance on the NVIDIA GeForce RTX 5060 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 attention computation and set --tensor-parallelism to 1 to avoid overloading the GPU.

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 --flash-attn is enabled and check that your CUDA installation is up to date.

Model fails to load

Verify the integrity of the downloaded model file and try re-downloading it.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a more user-friendly interface but may require more system resources. llama.cpp is highly customizable and can be fine-tuned for specific tasks, while Jan provides a lightweight solution suitable for less powerful systems. Choose based on your specific needs and system configuration.

Full Qwen 2.5 14B details →

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FAQ (20)

What GPU do I need to run Qwen 2.5 14B?

To run Qwen 2.5 14B, you need a GPU with at least 8.9 GB of VRAM, but 15.1 GB is recommended for optimal performance, especially for larger context lengths and higher precision.

Is Qwen 2.5 14B good for coding?

Yes, Qwen 2.5 14B is excellent for coding tasks, offering strong performance in generating code, understanding complex programming concepts, and providing detailed explanations.

Qwen 2.5 14B vs Llama 3.1 8B?

Qwen 2.5 14B has more parameters (14B vs 8B), which generally results in better performance in complex tasks like coding and reasoning, but requires more VRAM and computational resources.

Can I run Qwen 2.5 14B on a Mac?

Yes, you can run Qwen 2.5 14B on a Mac, but ensure your Mac has a compatible GPU with sufficient VRAM. M1/M2 chips with Metal support can also run the model efficiently.

How much VRAM does Qwen 2.5 14B need?

Qwen 2.5 14B requires between 8.9 GB and 15.1 GB of VRAM, depending on the quantization level used. Higher quantization levels reduce VRAM usage but may slightly impact performance.

Is Qwen 2.5 14B censored?

Qwen 2.5 14B is not inherently censored, but it adheres to ethical guidelines and content policies to ensure responsible use and avoid harmful or inappropriate content.

Is Qwen 2.5 14B commercial-use allowed?

Yes, Qwen 2.5 14B is licensed under the Apache-2.0 license, which allows commercial use as long as you comply with the terms of the license.

Qwen 2.5 14B context length?

Qwen 2.5 14B supports a context length of up to 131,072 tokens, making it suitable for handling very long documents and conversations.

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