Can RTX 3080 Ti run Qwen 2.5 14B?

Yes — runs locally

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

Your VRAM

12 GB

Model size

14B

Best quant

Q4_K_M

VRAM needed

8.9 GB

The verdict

The RTX 3080 Ti (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 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 3080 Ti

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

TL;DR

Qwen 2.5 14B runs well on the NVIDIA GeForce RTX 3080 Ti with a Grade A performance at ~48 tok/sec using the Q4_K_M quantization. This setup is highly recommended for its efficiency and responsiveness.

Prerequisites

Before starting, ensure you have at least 15GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 510.47.03 or later) installed along with CUDA 11.2 or higher.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 48 tokens per second, using around 8.9GB of VRAM. This leaves about 3.1GB of VRAM for context, allowing for a practical context window of up to 32,768 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.gguf --n-gpu-layers 12 --flash-attn --tensor-parallelism 1

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use the --n-gpu-layers 12 flag to offload layers to the GPU, enable --flash-attn for faster attention computations, and set --tensor-parallelism 1 to avoid splitting the model across multiple GPUs. This configuration ensures that the model runs efficiently within the 12GB VRAM limit.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 8 or 10.

Slow inference speed

Ensure that --flash-attn is enabled and that your CUDA installation is up to date. Also, check if there are any background processes consuming GPU resources.

Model fails to load

Verify that the model file has been downloaded correctly and that the file path specified in the run command is correct.

Alternative runtimes

While Ollama is the recommended runtime for this setup, you can also consider LM Studio for a more user-friendly interface, or llama.cpp for more advanced customization options. Jan is another viable option if you need a lightweight runtime, but it may not offer the same level of performance tuning as Ollama.

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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