Can RTX 5090 run SDXL Turbo (GGUF)?
Yes — runs locally
~168 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles SDXL Turbo (GGUF) comfortably using the Q5_0 quantization, which fits in 5.0 GB. Expected throughput is around 168 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
The SDXL Turbo (GGUF) model runs at Grade S on an NVIDIA GeForce RTX 5090 with Q5_0 quantization, achieving ~309 tok/sec.
Prerequisites
Before starting, ensure you have at least 3.5GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA driver (version 525.60 or later), and CUDA 11.8 or later installed.
Expected performance
With the Q5_0 quantization, you can expect the model to run at ~309 tok/sec, using approximately 5.0GB of VRAM. The remaining 27.0GB of VRAM provides ample headroom for processing larger contexts, enabling high-resolution image generation without memory constraints.
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 --model gpustack/stable-diffusion-xl-1.0-turbo-GGUF --quantization Q5_0
ollama interactive4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU. Enable flash attention (--flash-attn) to speed up computations. Given the 32GB VRAM, you can allocate up to 5.0GB for the model, leaving 27.0GB for context, which allows for handling large images or batches efficiently.
Troubleshooting
Out of memory error during inference
Reduce the number of layers offloaded to the GPU using --n-gpu-layers or decrease the batch size.
Slow inference times
Ensure that flash attention is enabled with --flash-attn and that the CUDA toolkit is up to date.
Model not found
Verify the model path and ensure the model is correctly downloaded and accessible.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for users who prefer a graphical interface and need more control over the environment. llama.cpp is ideal for low-level customization and fine-grained control over the model execution. Jan is a lightweight runtime for quick prototyping and testing, but may not offer the same level of performance optimization as Ollama.
Other models that run great on RTX 5090
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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