Can RTX 4070 SUPER run SDXL Turbo (GGUF)?

Yes — runs locally

~94 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 4070 SUPER (12 GB VRAM) handles SDXL Turbo (GGUF) comfortably using the Q5_0 quantization, which fits in 5.0 GB. Expected throughput is around 94 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 4070 SUPER

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

TL;DR

The SDXL Turbo (GGUF) model runs at Grade S on the NVIDIA GeForce RTX 4070 SUPER with Q5_0 quantization, achieving ~116 tok/sec.

Prerequisites

Before starting, ensure you have at least 3.5GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.85.12 or later), and CUDA 11.8 or later installed.

Expected performance

With the recommended settings, you can expect the model to run at ~116 tok/sec, using approximately 5.0GB of VRAM, 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 being generated.

1. Install runtimeOllama

pip install ollama
ollama init

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 gpustack/stable-diffusion-xl-1.0-turbo-GGUF:stable-diffusion-xl-1.0-turbo-Q5_0.gguf --n-gpu-layers 32 --flash-attn --tensor-parallelism 1

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, set --n-gpu-layers to 32 to utilize the available VRAM efficiently. Enable --flash-attn to speed up attention computations. Given the 12GB VRAM, you can set --tensor-parallelism to 1 for balanced performance without overloading the GPU.

Troubleshooting

Out of memory errors during inference

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

Slow inference times

Ensure --flash-attn is enabled and try increasing --tensor-parallelism to 2 if your GPU supports it.

Model not loading

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

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can also be used. LM Studio is suitable for a more user-friendly interface, while llama.cpp offers more fine-grained control over optimizations. Jan is ideal for distributed setups. Choose based on your specific needs and comfort level with the tools.

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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