Can RTX 4080 SUPER run SDXL Turbo (GGUF)?

Yes — runs locally

~114 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

3.5B

Best quant

Q5_0

VRAM needed

5.0 GB

The verdict

The RTX 4080 SUPER (16 GB VRAM) handles SDXL Turbo (GGUF) comfortably using the Q5_0 quantization, which fits in 5.0 GB. Expected throughput is around 114 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Single-step SDXL. Near-instant image generation.

Setup tutorial: SDXL Turbo (GGUF) on RTX 4080 SUPER

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

TL;DR

The SDXL Turbo (Q5_0) runs at Grade S on an NVIDIA GeForce RTX 4080 SUPER, achieving ~155 tok/sec with 5.0GB VRAM usage.

Prerequisites

Before starting, ensure you have at least 3.5GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

You can expect the model to run at ~155 tok/sec with 5.0GB VRAM in use, leaving 11.0GB of VRAM for context. This headroom allows for a significant context window, enhancing the quality and coherence of generated images.

1. Install runtimeOllama

pip install ollama
ollama config set cuda=True

2. Download the model

Download the SDXL Turbo (Q5_0) model, which is a 3.5GB file from the Hugging Face repository.

ollama pull gpustack/stable-diffusion-xl-1.0-turbo-GGUF:stable-diffusion-xl-1.0-turbo-Q5_0.gguf

3. Run it

ollama run stable-diffusion-xl-1.0-turbo-Q5_0 --n-gpu-layers 32 --flash-attn
ollama chat stable-diffusion-xl-1.0-turbo-Q5_0

4. Optimize for RTX 4080 SUPER

For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, use --n-gpu-layers 32 to offload layers to the GPU. Enable --flash-attn for faster attention computation. With 5.0GB VRAM used by the model, you have 11.0GB of VRAM left for context, allowing for a large practical context window.

Troubleshooting

Out of memory errors during inference.

Reduce --n-gpu-layers to 24 or 16 to decrease VRAM usage.

Slow inference speed.

Ensure CUDA is enabled and --flash-attn is set to True.

Model fails to load.

Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.

Alternative runtimes

Alternative runtimes include LM Studio and llama.cpp. LM Studio is suitable for a more user-friendly interface and additional model management features. llama.cpp offers more control over low-level optimizations and is ideal for advanced users. For this GPU, Ollama provides a balanced approach with good performance and ease of use.

Full SDXL Turbo (GGUF) details →

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FAQ (20)

What GPU do I need to run SDXL Turbo (GGUF)?

To run SDXL Turbo (GGUF), you need a GPU with at least 5.0 GB of VRAM. The exact VRAM requirement can vary slightly depending on the quantization level used.

Is SDXL Turbo (GGUF) good for coding?

SDXL Turbo (GGUF) is primarily designed for image generation, not coding. It may not be suitable for text-based programming tasks.

SDXL Turbo (GGUF) vs Llama 3.1 8B?

SDXL Turbo (GGUF) has 3.5 billion parameters and is optimized for fast image generation, while Llama 3.1 8B is a larger language model with 8 billion parameters, better suited for text generation tasks.

Can I run SDXL Turbo (GGUF) on a Mac?

Yes, you can run SDXL Turbo (GGUF) on a Mac as long as your Mac has a compatible GPU with at least 5.0 GB of VRAM.

How much VRAM does SDXL Turbo (GGUF) need?

SDXL Turbo (GGUF) requires at least 5.0 GB of VRAM, with the exact amount depending on the quantization level used.

Is SDXL Turbo (GGUF) censored?

The content generated by SDXL Turbo (GGUF) is not inherently censored, but it adheres to the community guidelines set by Stability AI.

Is SDXL Turbo (GGUF) commercial-use allowed?

Yes, SDXL Turbo (GGUF) is licensed under the stability-community license, which allows for commercial use, provided you adhere to the terms of the license.

SDXL Turbo (GGUF) context length?

The context length for SDXL Turbo (GGUF) is unknown, as it is primarily an image generation model and does not rely on text context in the same way as language models.

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