Can RTX 4070 SUPER run Qwen 2.5 14B?

Yes — runs locally

~36 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

12 GB

Model size

14B

Best quant

Q4_K_M

VRAM needed

8.9 GB

The verdict

The RTX 4070 SUPER (12 GB VRAM) handles Qwen 2.5 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. Strong 14B model with excellent coding and reasoning. iPad Pro recommended.

Setup tutorial: Qwen 2.5 14B on RTX 4070 SUPER

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

TL;DR

Qwen 2.5 14B runs exceptionally well on the NVIDIA GeForce RTX 4070 SUPER with a Grade A performance, using the Q4_K_M quantization. Expect ~48 tok/sec and snappy interactions.

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 525.60.11 or later) with CUDA 11.8 installed.

Expected performance

With the Q4_K_M quantization, you can expect ~48 tok/sec performance and 8.9GB VRAM usage, leaving 3.1GB of VRAM for context. This allows for a practical context window of around 10,000 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 12 --flash-attn
ollama chat Qwen2.5-14B-Instruct-Q4_K_M

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, set --n-gpu-layers to 12 to utilize the available VRAM efficiently. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With these settings, you should achieve ~48 tok/sec while maintaining a practical context window.

Troubleshooting

Out of memory errors during inference

Reduce the number of GPU layers (--n-gpu-layers) or decrease the batch size. For example, try --n-gpu-layers 10.

Slow inference times

Ensure that flash attention is enabled (--flash-attn) and that your CUDA installation is up to date.

Model fails to load

Verify that the model file has been downloaded correctly and that there are no issues with the Ollama runtime. Try re-downloading the model or reinstalling Ollama.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you prefer a different interface or need more customization options. LM Studio is ideal for a graphical interface, llama.cpp offers more control over quantization and performance tuning, and Jan is suitable for lightweight deployments. However, Ollama provides a simple and efficient way to run Qwen 2.5 14B on the RTX 4070 SUPER.

Full Qwen 2.5 14B details →

Other models that run great on RTX 4070 SUPER

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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