Can M4 Max run SDXL Turbo (GGUF)?

Yes — runs locally

~74 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

128 GB

Model size

3.5B

Best quant

Q5_0

VRAM needed

5.0 GB

The verdict

The M4 Max (128 GB VRAM) handles SDXL Turbo (GGUF) comfortably using the Q5_0 quantization, which fits in 5.0 GB. Expected throughput is around 74 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 Max

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

TL;DR

SDXL Turbo (Q5_0) runs at Grade S on the Apple M4 Max, achieving ~530 tok/sec with 5.0GB VRAM usage and 123.0GB of headroom.

Prerequisites

Before starting, ensure you have at least 10GB 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 the terminal.

Expected performance

With the Q5_0 quantization, you can expect ~530 tok/sec, using 5.0GB of VRAM, leaving 123.0GB of headroom for context. This allows for a practical context window of up to 2048 tokens, depending on the complexity of the generated images.

1. Install runtimeOllama (preferred on Apple Silicon)

brew install ollama
ollama init

2. Download the model

Download the Q5_0 quantized model (3.5GB) 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 stable-diffusion-xl-1.0-turbo-Q5_0 --device mps --context-length 2048

4. Optimize for M4 Max

For optimal performance on the Apple M4 Max, use the Metal Performance Shaders (MPS) backend with the MLX runtime. The 128GB of unified memory allows for efficient data transfer between CPU and GPU, ensuring that the 5.0GB VRAM requirement is met without bottlenecks. Set the context length to 2048 to maximize the practical context window while maintaining performance.

Troubleshooting

Error: 'MPS device not found'

Ensure that the Metal Performance Shaders (MPS) framework is installed and enabled. Run `sudo softwareupdate --install-rosetta` to install Rosetta if needed.

Low performance or high latency

Check if the model is running on the correct device by adding `--device mps` to the run command. Ensure that the context length is set appropriately for your use case.

Out of memory errors

Reduce the context length or batch size. For example, try `--context-length 1024` to reduce memory usage.

Alternative runtimes

While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio, llama.cpp, or MLX. LM Studio is suitable for GUI-based workflows, llama.cpp offers more fine-grained control over inference, and MLX is ideal for integrating with existing Metal-based applications. Choose the runtime based on your specific use case and development environment.

Full SDXL Turbo (GGUF) details →

Other models that run great on M4 Max

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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