Can RTX 3060 12GB run Stable Diffusion XL (CoreML)?

Yes — runs locally

~58 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

12 GB

Model size

3.5B

Best quant

CoreML

VRAM needed

3.3 GB

The verdict

The RTX 3060 12GB (12 GB VRAM) handles Stable Diffusion XL (CoreML) comfortably using the CoreML quantization, which fits in 3.3 GB. Expected throughput is around 58 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. 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 3060 12GB

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

TL;DR

Run Stable Diffusion XL (CoreML) on an NVIDIA GeForce RTX 3060 12GB for Grade S performance at ~174 tok/sec using 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 470 or later), and CUDA 11.2 or later installed.

Expected performance

With the recommended settings, you should expect ~174 tok/sec performance, utilizing 3.3GB of VRAM, leaving 8.7GB of headroom for context. This headroom allows for a practical context window that can handle larger images or more complex prompts.

1. Install runtimeOllama

pip install ollama
ollama init

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

4. Optimize for RTX 3060 12GB

For optimal performance on the NVIDIA GeForce RTX 3060 12GB, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 32 to balance between VRAM usage and speed. Enable flash attention (--flash-attn) to reduce memory usage and improve performance. With 12GB VRAM, you can achieve a practical context window that maximizes the use of available VRAM without running out of memory.

Troubleshooting

Out of Memory (OOM) errors during inference

Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 24 or 16.

Slow inference times

Ensure that flash attention is enabled with --flash-attn. If still slow, try reducing the batch size or increasing the number of CPU threads.

Model fails to load

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

Alternative runtimes

For users who prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Ollama is recommended for its ease of use and robust support for the CoreML quantization on this GPU.

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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