Can RTX 5080 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 5080 (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 5080

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

TL;DR

Stable Diffusion XL (CoreML) runs at Grade S on an NVIDIA GeForce RTX 5080, achieving ~231 tok/sec with the CoreML quantization (2.8GB file, 3.3GB VRAM).

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the CoreML quantization, you can expect ~231 tok/sec and 3.3GB VRAM in use, leaving 12.7GB of VRAM for context. This allows for a practical context window of several thousand tokens, depending on the complexity of the images generated.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the CoreML quantized version of Stable Diffusion XL (2.8GB file) 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 5080

For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 16GB VRAM, you can allocate up to 3.3GB for the model, leaving 12.7GB for context and other operations.

Troubleshooting

Out of memory errors during inference

Reduce the number of layers offloaded to the GPU using --n-gpu-layers or enable flash attention with --flash-attn.

Slow inference times

Ensure CUDA is properly configured and try increasing the batch size if your VRAM allows it.

Model fails to load

Verify that the model files are correctly downloaded and not corrupted. Re-run the download command if necessary.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the execution environment or specific features. LM Studio is ideal for a GUI-based workflow, while llama.cpp offers low-level customization and performance tuning. Jan is suitable for distributed training and large-scale deployments.

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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