Can RTX 4070 SUPER run Stable Diffusion XL (CoreML)?
Yes — runs locally
~94 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 SUPER (12 GB VRAM) handles Stable Diffusion XL (CoreML) comfortably using the CoreML quantization, which fits in 3.3 GB. Expected throughput is around 94 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 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Stable Diffusion XL (CoreML) runs at Grade S on an NVIDIA GeForce RTX 4070 SUPER, achieving ~174 tok/sec with the recommended CoreML quantization (2.8GB file, 3.3GB VRAM).
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows 10/11 or Linux), and the latest NVIDIA drivers (version 525.60.12 or later) installed along with CUDA 11.8.
Expected performance
With the recommended settings, you can expect ~174 tok/sec and 3.3GB VRAM in use, leaving 8.7GB of VRAM for context. This allows for a practical context window of up to 2048 tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. 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-ios3. Run it
ollama run apple/coreml-stable-diffusion-xl-base-ios --interactive
ollama generate apple/coreml-stable-diffusion-xl-base-ios --prompt 'Your prompt here'4. Optimize for RTX 4070 SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 32 to balance between speed and memory usage. Enable flash attention (--flash-attn) to further optimize performance. With 3.3GB VRAM used by the model, you have approximately 8.7GB of VRAM headroom for larger context windows or additional tasks.
Troubleshooting
Out of Memory (OOM) errors during inference.
Reduce the --n-gpu-layers value to 24 or 16 to lower VRAM usage.
Slow inference times.
Ensure CUDA is properly installed and configured. Use --flash-attn to speed up attention calculations.
Model fails to load.
Check if the model file is corrupted or incomplete. Re-download the model using the provided command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for different use cases. LM Studio is ideal for a graphical interface and easy model management, llama.cpp is lightweight and suitable for embedded systems, and Jan offers advanced features for large-scale deployments. However, Ollama provides a balanced approach with good performance and ease of use, making it the recommended choice for the NVIDIA GeForce RTX 4070 SUPER.
Other models that run great on RTX 4070 SUPER
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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