Can RTX 4090 run SDXL Turbo (GGUF)?
Yes — runs locally
~144 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (24 GB VRAM) handles SDXL Turbo (GGUF) comfortably using the Q5_0 quantization, which fits in 5.0 GB. Expected throughput is around 144 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 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run SDXL Turbo (Q5_0) on an NVIDIA GeForce RTX 4090 for Grade S performance at ~232 tok/sec. Requires 5.0GB VRAM, leaving ample headroom.
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 525.60 or later), and CUDA 11.8 or later installed.
Expected performance
With the specified configuration, you can expect ~232 tok/sec performance, utilizing 5.0GB of VRAM. The remaining 19.0GB of VRAM provides significant headroom for handling larger context windows, allowing for more complex and detailed image generation tasks.
1. Install runtimeOllama
pip install ollama
ollama init2. 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.gguf3. Run it
ollama run stable-diffusion-xl-1.0-turbo-Q5_0 --n-gpu-layers 32 --flash-attn --tensor-parallelism 24. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to distribute the workload across multiple cores. This configuration ensures that the 5.0GB VRAM required by the model is used effectively, leaving 19.0GB of VRAM for context and other tasks.
Troubleshooting
Out of memory errors during model loading
Reduce --n-gpu-layers to 16 or 24 to lower VRAM usage.
Slow performance
Ensure CUDA is correctly installed and update your NVIDIA drivers to the latest version.
Model fails to load
Verify the integrity of the downloaded model file and try re-downloading it.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for specific use cases. LM Studio offers a user-friendly interface and is suitable for beginners, while llama.cpp provides more control over low-level optimizations and is ideal for advanced users. Jan is lightweight and efficient, making it a good choice for resource-constrained environments. However, Ollama is recommended for its ease of use and robust performance on the NVIDIA GeForce RTX 4090.
Other models that run great on RTX 4090
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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