Can RTX 5060 Ti run Stable Diffusion XL (CoreML)?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5060 Ti (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 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Stable Diffusion XL (CoreML) runs at Grade S on an NVIDIA GeForce RTX 5060 Ti, achieving ~231 tok/sec with the CoreML quantization. This setup is highly optimized for high-performance image generation.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (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 to achieve ~231 tok/sec, utilizing 3.3GB of VRAM. The remaining 12.7GB of VRAM provides ample headroom for handling large context windows, enabling high-quality image generation even with complex prompts.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the CoreML quantized model (2.8GB) from Hugging Face.
ollama pull apple/coreml-stable-diffusion-xl-base-ios:coreml-stable-diffusion-xl-base-ios_split_einsum_compiled.zip3. Run it
ollama run apple/coreml-stable-diffusion-xl-base-ios --device cuda
ollama interactive apple/coreml-stable-diffusion-xl-base-ios4. Optimize for RTX 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti 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 approximately 12.7GB of VRAM left for context, allowing for larger batch sizes and longer sequences.
Troubleshooting
Out of memory errors during inference
Reduce the batch size or enable gradient checkpointing with --gradient-checkpointing
Slow inference times
Ensure CUDA is properly installed and configured. Use the --flash-attn flag to optimize memory usage and speed.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
For users who prefer different runtimes, 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 performance optimization, especially on GPUs like the NVIDIA GeForce RTX 5060 Ti.
Other models that run great on RTX 5060 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.
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