Can M4 Pro run SDXL Turbo (GGUF)?

Yes — runs locally

~62 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

48 GB

Model size

3.5B

Best quant

Q5_0

VRAM needed

5.0 GB

The verdict

The M4 Pro (48 GB VRAM) handles SDXL Turbo (GGUF) comfortably using the Q5_0 quantization, which fits in 5.0 GB. Expected throughput is around 62 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 M4 Pro

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

TL;DR

SDXL Turbo (Q5_0) runs at Grade S on the Apple M4 Pro, achieving ~199 tok/sec with 5.0GB VRAM usage and 43.0GB headroom for context.

Prerequisites

Before starting, ensure you have at least 8GB of free disk space, macOS 12.3 or later, and Xcode Command Line Tools installed. You can install Xcode CLT by running `xcode-select --install` in your terminal.

Expected performance

With the Q5_0 quantization, you can expect the model to run at ~199 tok/sec, using 5.0GB of VRAM. The 43.0GB of remaining VRAM allows for a large practical context window, enabling the generation of high-quality images with minimal latency.

1. Install runtimeOllama (preferred on Apple Silicon)

brew install ollama
ollama init

2. Download the model

Download the Q5_0 quantized version of SDXL Turbo, which is a 3.5GB file.

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

3. Run it

ollama run stable-diffusion-xl-1.0-turbo-Q5_0 --device metal
ollama chat --model stable-diffusion-xl-1.0-turbo-Q5_0

4. Optimize for M4 Pro

To optimize performance on the Apple M4 Pro, use the Metal/MLX backend with MPS layers to leverage the 48GB of unified memory. This will ensure that the model runs efficiently, utilizing the 43.0GB of remaining VRAM for larger context windows and more complex tasks.

Troubleshooting

If you encounter an 'Out of Memory' error, try reducing the batch size or context length.

ollama run stable-diffusion-xl-1.0-turbo-Q5_0 --device metal --batch-size 1

If the model runs slowly, ensure that the Metal/MLX backend is correctly configured.

ollama config set device metal

If you see a 'Device not found' error, check your GPU drivers and ensure they are up to date.

brew upgrade ollama && ollama init

Alternative runtimes

While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio for a GUI-based experience, llama.cpp for more control over quantization, or MLX for direct Metal integration. Jan is another option but may not offer the same level of optimization for the Apple M4 Pro.

Full SDXL Turbo (GGUF) details →

Other models that run great on M4 Pro

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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