Can RTX 4080 SUPER run Stable Diffusion XL (CoreML)?

Yes — runs locally

~114 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

3.5B

Best quant

CoreML

VRAM needed

3.3 GB

The verdict

The RTX 4080 SUPER (16 GB VRAM) handles Stable Diffusion XL (CoreML) comfortably using the CoreML quantization, which fits in 3.3 GB. Expected throughput is around 114 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Higher quality image generation. CoreML optimized for iOS. Requires 6GB+ usable memory (iPad/Mac recommended).

Setup tutorial: Stable Diffusion XL (CoreML) on RTX 4080 SUPER

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

TL;DR

Stable Diffusion XL (CoreML) runs at Grade S on the NVIDIA GeForce RTX 4080 SUPER with ~231 tok/sec using the CoreML quantization (2.8GB file).

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows 10/11 or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8.

Expected performance

You can expect the model to run at approximately 231 tokens per second, using around 3.3GB of VRAM. With 12.7GB of VRAM headroom, you can achieve a practical context window of several thousand tokens, depending on the complexity of the images generated.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the CoreML quantized model (2.8GB) from Hugging Face.

ollama pull apple/coreml-stable-diffusion-xl-base-ios

3. Run it

ollama run apple/coreml-stable-diffusion-xl-base-ios
ollama generate --model apple/coreml-stable-diffusion-xl-base-ios --prompt 'Your prompt here'

4. Optimize for RTX 4080 SUPER

For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 3.3GB VRAM used by the model, you will have approximately 12.7GB of VRAM headroom for larger context windows or additional layers.

Troubleshooting

Out of memory errors during inference.

Reduce the number of --n-gpu-layers or disable --flash-attn to lower VRAM usage.

Slow inference times.

Ensure CUDA is properly installed and enabled in your environment. Check that the NVIDIA drivers are up to date.

Model fails to load.

Verify the integrity of the downloaded model files and try re-downloading the model using the 'ollama pull' command.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight and portable deployment, or Jan for advanced customization options. Ollama is recommended for its ease of use and robust support for CoreML models on NVIDIA GPUs.

Full Stable Diffusion XL (CoreML) details →

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FAQ (20)

What GPU do I need to run Stable Diffusion XL (CoreML)?

Stable Diffusion XL (CoreML) is optimized for iOS devices and does not require a specific GPU. It leverages the Metal Performance Shaders (MPS) framework on iPad and Mac.

Is Stable Diffusion XL (CoreML) good for coding?

Stable Diffusion XL (CoreML) is primarily designed for generating high-quality images and is not optimized for coding tasks.

Stable Diffusion XL (CoreML) vs Llama 3.1 8B?

Stable Diffusion XL (CoreML) is an image generation model with 3.5B parameters, while Llama 3.1 8B is a text generation model with 8B parameters. They serve different purposes and are not directly comparable.

Can I run Stable Diffusion XL (CoreML) on a Mac?

Yes, Stable Diffusion XL (CoreML) can run on a Mac, but it requires at least 6GB of usable memory and is optimized for iOS devices like iPads.

How much VRAM does Stable Diffusion XL (CoreML) need?

Stable Diffusion XL (CoreML) requires 3.3 GB of VRAM, which is consistent across different quantization levels.

Is Stable Diffusion XL (CoreML) censored?

The model is not inherently censored, but it adheres to the CreativeML OpenRail-M license, which may have usage guidelines and restrictions.

Is Stable Diffusion XL (CoreML) commercial-use allowed?

Yes, Stable Diffusion XL (CoreML) can be used commercially, but you must comply with the terms of the CreativeML OpenRail-M license.

Stable Diffusion XL (CoreML) context length?

The context length for Stable Diffusion XL (CoreML) is not specified, as it is primarily an image generation model and does not handle text input in the same way as language models.

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