Can RTX 4060 Ti 16GB run SDXL Turbo (GGUF)?

Yes — runs locally

~78 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 4060 Ti 16GB (16 GB VRAM) handles SDXL Turbo (GGUF) comfortably using the Q5_0 quantization, which fits in 5.0 GB. Expected throughput is around 78 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 4060 Ti 16GB

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

TL;DR

Run SDXL Turbo (Q5_0) on an NVIDIA GeForce RTX 4060 Ti 16GB for Grade S performance at ~155 tok/sec. Requires 5.0GB VRAM.

Prerequisites

Before starting, ensure you have at least 3.5GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 510.47 or later, and CUDA 11.2 or later installed.

Expected performance

With the recommended settings, you can expect ~155 tok/sec and 5.0GB VRAM usage, leaving 11.0GB of VRAM for context. Given the remaining VRAM, you can achieve a practical context window of several thousand tokens, depending on the specific requirements of your task.

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 --quantization Q5_0 --gpu

4. Optimize for RTX 4060 Ti 16GB

For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 32 to fully utilize the 16GB VRAM. Enable flash-attn for faster inference and consider using tensor parallelism if running multiple instances. This configuration will allow you to achieve the target ~155 tok/sec.

Troubleshooting

Out of memory errors during inference.

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

Slow inference times.

Ensure flash-attn is enabled and check that the CUDA drivers are up to date.

Model fails to load.

Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for specific use cases. LM Studio offers a more user-friendly interface, while llama.cpp provides more control over low-level optimizations. Jan is suitable for distributed inference across multiple GPUs. For the NVIDIA GeForce RTX 4060 Ti 16GB, Ollama is generally the most straightforward and performant option.

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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