Can RTX 3070 Ti run Stable Diffusion XL (CoreML)?

Yes — runs locally

~60 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

8 GB

Model size

3.5B

Best quant

CoreML

VRAM needed

3.3 GB

The verdict

The RTX 3070 Ti (8 GB VRAM) handles Stable Diffusion XL (CoreML) comfortably using the CoreML quantization, which fits in 3.3 GB. Expected throughput is around 60 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 3070 Ti

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 3070 Ti with ~116 tok/sec using the CoreML quantization. Requires 6GB+ usable memory.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible OS (Windows 10/11 or Linux), the latest NVIDIA drivers (version 470 or later), and CUDA 11.2 or later installed.

Expected performance

You can expect the model to run at approximately 116 tok/sec with 3.3GB VRAM in use, leaving 4.7GB of VRAM for context. This should allow for a practical context window of up to 2048 tokens, depending on the complexity of the input.

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

3. Run it

ollama run apple/coreml-stable-diffusion-xl-base-ios
ollama interactive apple/coreml-stable-diffusion-xl-base-ios

4. Optimize for RTX 3070 Ti

For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, set --n-gpu-layers to 30 to maximize GPU utilization while keeping VRAM usage under control. Enable flash-attn to reduce memory overhead and improve speed. With 3.3GB VRAM used by the model, you have 4.7GB of headroom for context, allowing for a practical context window of up to 2048 tokens.

Troubleshooting

Out of memory errors during inference

Reduce --n-gpu-layers to 20 or lower and decrease batch size.

Slow inference times

Ensure CUDA is properly installed and enable flash-attn.

Model fails to load

Check if the model files are corrupted and re-download the model using the 'ollama pull' command.

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 compatibility with the CoreML quantization on the NVIDIA GeForce RTX 3070 Ti.

Full Stable Diffusion XL (CoreML) details →

Other models that run great on RTX 3070 Ti

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.

Want personalized recommendations for your exact setup? Detect my hardware →