Can RTX 3090 Ti run Qwen 2.5 14B?

Yes — runs locally

~42 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

24 GB

Model size

14B

Best quant

Q8_0

VRAM needed

15.1 GB

The verdict

The RTX 3090 Ti (24 GB VRAM) handles Qwen 2.5 14B comfortably using the Q8_0 quantization, which fits in 15.1 GB. Expected throughput is around 42 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 3090 Ti

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

TL;DR

Run Qwen 2.5 14B on an NVIDIA GeForce RTX 3090 Ti with Grade S performance at ~57 tok/sec using the Q8_0 quantization.

Prerequisites

Before starting, ensure you have at least 15GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 470 or later, and CUDA 11.0 or later installed.

Expected performance

With the Q8_0 quantization, you can expect the model to run at approximately 57 tokens per second, utilizing around 15.1GB of VRAM. This leaves you with about 8.9GB of VRAM for context, allowing for a practical context window of up to 131,072 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 Q8_0 quantized model (14.6GB file) from Hugging Face.

ollama pull bartowski/Qwen2.5-14B-Instruct-GGUF:Qwen2.5-14B-Instruct-Q8_0.gguf

3. Run it

ollama run Qwen2.5-14B-Instruct-Q8_0 --n-gpu-layers 14 --flash-attn --tensor-parallelism 1

4. Optimize for RTX 3090 Ti

For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use --n-gpu-layers 14 to maximize the number of layers offloaded to the GPU. Enable --flash-attn for faster attention computation and set --tensor-parallelism 1 to utilize the full VRAM efficiently. This configuration ensures that the model runs smoothly within the 24GB VRAM limit.

Troubleshooting

Out of memory error during inference

Reduce the number of --n-gpu-layers or increase the batch size to fit within the available VRAM.

Slow inference speed

Ensure that --flash-attn is enabled and try increasing the --tensor-parallelism value if your GPU supports it.

Model fails to load

Verify that the model file is downloaded correctly and that the Ollama runtime is properly installed and initialized.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over quantization and performance settings, or Jan for lightweight deployment. Each has its own strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 3090 Ti.

Full Qwen 2.5 14B details →

Other models that run great on RTX 3090 Ti

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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