Can RTX 5090 run Stable Diffusion XL (CoreML)?

Yes — runs locally

~168 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

32 GB

Model size

3.5B

Best quant

CoreML

VRAM needed

3.3 GB

The verdict

The RTX 5090 (32 GB VRAM) handles Stable Diffusion XL (CoreML) comfortably using the CoreML quantization, which fits in 3.3 GB. Expected throughput is around 168 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 5090

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

TL;DR

Run Stable Diffusion XL (CoreML) on an NVIDIA GeForce RTX 5090 with Grade S performance, using the CoreML quantization. Expect ~463 tok/sec and 3.3GB VRAM usage.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows 10/11 or Linux), the latest NVIDIA drivers (version 525.60.11 or later), and CUDA 11.8 installed.

Expected performance

With the CoreML quantization, expect a token generation rate of approximately 463 tok/sec, utilizing 3.3GB of the 32GB VRAM. This leaves 28.7GB of VRAM available, which can support a practical context window of several thousand tokens, depending on the complexity of the images being generated.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

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

ollama pull apple/coreml-stable-diffusion-xl-base-ios:coreml-stable-diffusion-xl-base-ios_split_einsum_compiled.zip

3. Run it

ollama run apple/coreml-stable-diffusion-xl-base-ios --device cuda
ollama interact apple/coreml-stable-diffusion-xl-base-ios

4. 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. With 3.3GB VRAM used, you have 28.7GB of VRAM headroom, allowing for larger context windows and more complex tasks.

Troubleshooting

Insufficient VRAM allocation

Increase the VRAM allocation by setting --gpu-memory to a higher value, e.g., --gpu-memory 24G.

Slow token generation

Enable flash attention by adding the --flash-attn flag to your run command.

Model fails to load

Ensure that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight and portable execution, or Jan for advanced customization options. Ollama is recommended for its ease of use and compatibility with the CoreML quantization on the NVIDIA GeForce RTX 5090.

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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