Can RTX 5070 Ti run Stable Diffusion XL (CoreML)?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5070 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 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Stable Diffusion XL (CoreML) runs at Grade S on an NVIDIA GeForce RTX 5070 Ti with ~231 tok/sec using the CoreML quantization. Requires 6GB+ usable memory, optimized for iOS but works well on NVIDIA GPUs.
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 510.73 or later), and CUDA 11.4 or later installed.
Expected performance
With the CoreML quantization, you can expect ~231 tok/sec and 3.3GB VRAM usage, leaving 12.7GB of VRAM headroom for larger context windows. Given the remaining VRAM, you can achieve a practical context window of several thousand tokens, depending on the complexity of the images generated.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. Download the model
Download the CoreML quantized model (2.8GB) from Hugging Face.
ollama pull apple/coreml-stable-diffusion-xl-base-ios3. 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 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 32 to utilize the full 16GB VRAM. Enable flash attention with --flash-attn to speed up inference. With 3.3GB VRAM used by the model, you have 12.7GB of VRAM headroom for larger context windows or additional layers.
Troubleshooting
Out of memory errors during inference
Reduce the --n-gpu-layers value to 24 or lower to reduce VRAM usage.
Slow inference times
Ensure CUDA is properly installed and configured. Use --flash-attn to enable faster inference.
Model not loading
Verify that the model files are correctly downloaded and not corrupted. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. LM Studio offers a more user-friendly interface and is suitable for users who prefer a GUI. llama.cpp is a lightweight option for those who need minimal dependencies. Jan is another runtime that supports a wide range of models but may require more manual configuration. For the NVIDIA GeForce RTX 5070 Ti, Ollama is recommended due to its ease of use and CUDA backend support.
Other models that run great on RTX 5070 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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