Can RTX 5060 run Qwen 2.5 14B?

Yes — runs locally

~0 tok/sec · Cannot run — model too large for this GPU

Your VRAM

8 GB

Model size

14B

Best quant

Q4_K_M

VRAM needed

8.9 GB

The verdict

The RTX 5060 (8 GB VRAM) handles Qwen 2.5 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. Strong 14B model with excellent coding and reasoning. iPad Pro recommended.

Setup tutorial: Qwen 2.5 14B on RTX 5060

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

TL;DR

Qwen 2.5 14B runs on the NVIDIA GeForce RTX 5060 with a Grade C performance, using the Q4_K_M quantization. Expect ~32 tokens/second with 8.9GB VRAM usage.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60.13 or later) installed. You also need CUDA 11.8 or later.

Expected performance

With the Q4_K_M quantization, you can expect a token generation rate of around 32 tokens/second. The model will use approximately 8.9GB of VRAM, leaving about -0.9GB for context. Given the limited VRAM, a practical context window of around 8K tokens is achievable.

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 12 --flash-attn
ollama chat Qwen2.5-14B-Instruct-Q4_K_M

4. Optimize for RTX 5060

For optimal performance on the NVIDIA GeForce RTX 5060 with 8GB VRAM, use --n-gpu-layers 12 to offload some layers to CPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 8.9GB VRAM required, you will have approximately -0.9GB of headroom for context, so consider reducing the context length if needed.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers with --n-gpu-layers 8 or lower.

Slow token generation

Ensure flash attention is enabled with --flash-attn and try increasing the batch size.

Model fails to load

Check if the model file is corrupted and re-download it using the 'ollama pull' command.

Alternative runtimes

If you prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for more control over optimizations, or Jan for better multi-GPU support. For the NVIDIA GeForce RTX 5060, Ollama provides a good balance of ease of use and performance.

Full Qwen 2.5 14B details →

Other models that run great on RTX 5060

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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