Can RTX 3080 Ti run SDXL Turbo (GGUF)?

Yes — runs locally

~74 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

12 GB

Model size

3.5B

Best quant

Q5_0

VRAM needed

5.0 GB

The verdict

The RTX 3080 Ti (12 GB VRAM) handles SDXL Turbo (GGUF) comfortably using the Q5_0 quantization, which fits in 5.0 GB. Expected throughput is around 74 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 3080 Ti

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

TL;DR

The SDXL Turbo (GGUF) model runs at Grade S on an NVIDIA GeForce RTX 3080 Ti with Q5_0 quantization, achieving ~116 tok/sec.

Prerequisites

Before starting, ensure you have at least 3.5GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 512.15 or later) installed along with CUDA 11.4 or higher.

Expected performance

With the Q5_0 quantization, you can expect ~116 tok/sec and 5.0GB VRAM in use, leaving 7.0GB of headroom for context. This allows for a practical context window of several hundred tokens, depending on the complexity of the images generated.

1. Install runtimeOllama

pip install ollama
ollama config set cuda=True

2. Download the model

Download the 3.5GB Q5_0 quantized model from Hugging Face.

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

3. Run it

ollama run --model stable-diffusion-xl-1.0-turbo-Q5_0.gguf --n-gpu-layers 30 --flash-attn
ollama interactive --model stable-diffusion-xl-1.0-turbo-Q5_0.gguf

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use --n-gpu-layers 30 to offload some layers to the CPU, enabling flash attention (--flash-attn) to reduce memory usage and improve speed. With 5.0GB VRAM in use, you have 7.0GB of headroom for larger context windows or additional models.

Troubleshooting

Out of memory errors during inference

Reduce --n-gpu-layers to 20 or lower and increase --cpu-layers accordingly.

Slow inference times

Ensure CUDA is enabled and flash attention is used: `ollama config set cuda=True` and add `--flash-attn` to your run command.

Model not found

Verify the model path and ensure the model is correctly downloaded: `ollama list`

Alternative runtimes

Alternative runtimes include LM Studio and llama.cpp. Use LM Studio for a more user-friendly interface and easier model management. Use llama.cpp for more advanced customization and control over the inference process, especially useful for fine-tuning or modifying the model. Jan is another option for those who prefer a lightweight, easy-to-deploy solution, but it may not offer the same level of performance tuning as Ollama.

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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