Can RTX 3080 Ti run Stable Diffusion XL (CoreML)?
Yes — runs locally
~74 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3080 Ti (12 GB VRAM) handles Stable Diffusion XL (CoreML) comfortably using the CoreML quantization, which fits in 3.3 GB. Expected throughput is around 74 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 3080 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Stable Diffusion XL (CoreML) runs at Grade S on an NVIDIA GeForce RTX 3080 Ti, achieving ~174 tok/sec with the CoreML quantization. Requires 6GB+ usable memory and is optimized for iOS devices.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible OS (Windows or Linux), and the latest NVIDIA drivers (version 470.82.01 or later) installed along with CUDA 11.4 or later.
Expected performance
With the CoreML quantization, you can expect ~174 tok/sec performance, utilizing approximately 3.3GB of VRAM. This leaves you with 8.7GB of VRAM headroom, allowing for a practical context window that can handle larger images or more complex prompts without running out of memory.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the CoreML quantized version of Stable Diffusion XL, which is a 2.8GB file.
ollama pull apple/coreml-stable-diffusion-xl-base-ios3. Run it
ollama run apple/coreml-stable-diffusion-xl-base-ios
ollama generate --model apple/coreml-stable-diffusion-xl-base-ios --prompt 'Your prompt here'4. Optimize for RTX 3080 Ti
For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable flash-attn for faster inference and consider using tensor parallelism to distribute the workload across multiple GPUs if available. This configuration will help maintain the ~174 tok/sec speed while keeping VRAM usage within the 12GB limit.
Troubleshooting
Out of memory errors during inference
Reduce the --n-gpu-layers value to 24 or 16 to lower VRAM usage.
Slow inference times
Ensure CUDA and NVIDIA drivers are up to date, and enable flash-attn to speed up the process.
Model fails to load
Verify the model file integrity by re-downloading it from the Hugging Face repository.
Alternative runtimes
Alternative runtimes like LM Studio and llama.cpp can be used for more advanced customization or if you prefer a different workflow. LM Studio offers a graphical interface and is suitable for users who prefer a visual setup, while llama.cpp provides a lightweight, command-line interface and is ideal for low-resource environments. For the NVIDIA GeForce RTX 3080 Ti, Ollama remains the most straightforward and performant option.
Other models that run great on RTX 3080 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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