Can RTX 4070 Ti SUPER run Stable Diffusion XL (CoreML)?

Yes — runs locally

~102 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 4070 Ti SUPER (16 GB VRAM) handles Stable Diffusion XL (CoreML) comfortably using the CoreML quantization, which fits in 3.3 GB. Expected throughput is around 102 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 4070 Ti SUPER

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 4070 Ti SUPER with ~231 tok/sec using the CoreML (2.8GB) quantization. This setup is highly optimized for high-speed image generation.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA drivers version 525.60 or later, and CUDA 11.8 or later installed on your system.

Expected performance

With the CoreML (2.8GB) quantization, you can expect ~231 tok/sec and 3.3GB VRAM in use, leaving 12.7GB of VRAM headroom for larger context windows. Given the remaining VRAM, you can achieve a practical context window of up to 2048 tokens without running into memory constraints.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the CoreML (2.8GB) quantized model 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 interactive apple/coreml-stable-diffusion-xl-base-ios

4. Optimize for RTX 4070 Ti SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 3.3GB VRAM used by the model, you will have 12.7GB of VRAM headroom for larger context windows and additional layers.

Troubleshooting

CUDA out of memory error

Reduce the number of GPU layers using --n-gpu-layers 24 or lower.

Slow inference speed

Ensure flash attention is enabled with --flash-attn and check that the latest NVIDIA drivers and CUDA are installed.

Model not found

Verify the model path and ensure the model is correctly downloaded using the ollama pull command.

Alternative runtimes

Alternative runtimes include LM Studio for a more user-friendly interface, llama.cpp for CPU-based inference, and Jan for web-based deployment. Use LM Studio for a graphical interface, llama.cpp for systems without a GPU, and Jan for deploying models on a web server. However, Ollama provides the best balance of performance and ease of use for 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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