Can RTX 4060 Ti Run Stable Diffusion XL?

Yes, the RTX 4060 Ti runs Stable Diffusion XL very well. With 16GB GDDR6 VRAM, the RTX 4060 Ti loads the SDXL base model in FP16 at its native 1024×1024 resolution with room to spare. This is a strong card for Stable Diffusion hosting and one of the most cost-effective options for SDXL workloads.

The Short Answer

YES. SDXL runs natively at 1024×1024 in FP16 with good speed.

SDXL’s base model weighs approximately 6.5GB in FP16 for the UNet, CLIP, and VAE combined. At 1024×1024 resolution, the latent tensors and intermediate activations add roughly 4GB during generation. Total peak VRAM usage sits around 10-11GB, well within the RTX 4060 Ti’s 16GB budget. You can even load LoRA models and use the refiner with sequential offloading.

The 16GB VRAM is the key differentiator over the base RTX 4060 (8GB), which cannot run SDXL at full resolution without heavy compromises.

VRAM Analysis

Running the base model with a LoRA leaves roughly 5GB free, enough for batch size 2 or higher resolutions up to about 1280×1280. The refiner model adds another 6GB but can be loaded sequentially (unload base, load refiner) to stay within budget. See our SDXL VRAM requirements page for detailed breakdowns.

Performance Benchmarks

SDXL base model, 1024×1024, 20 steps, Euler sampler, batch size 1:

*RTX 4060 requires –medvram-sdxl and runs at reduced speed due to memory offloading.

At 5.2 it/s, the RTX 4060 Ti generates an SDXL image in under 4 seconds, which is productive for iterative workflows. With SDXL Turbo (4 steps), generation drops to under 1 second per image. Check our benchmarks page for more comparisons.

Setup Guide

ComfyUI or Automatic1111 both handle SDXL well on the RTX 4060 Ti:

# Automatic1111 with xformers for speed
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui
python launch.py --xformers --listen --port 7860

Note that you do NOT need the --medvram flag on the 16GB card. The full model loads into VRAM without offloading. For ComfyUI:

git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
python main.py --listen 0.0.0.0 --port 8188

Place SDXL checkpoints in the appropriate models directory. The RTX 4060 Ti handles LoRA loading, ControlNet, and IPAdapter without memory issues at 1024×1024. For batch generation, keep batch size at 1-2 to stay within VRAM limits.

Recommended Alternative

The RTX 4060 Ti is already a solid SDXL card. If you need the refiner loaded simultaneously, higher resolutions, or larger batch sizes, the RTX 3090 with 24GB gives you the extra headroom. For newer diffusion models like Flux.1, which demand more VRAM, check whether the RTX 5080 can run SDXL for a speed upgrade.

If you are also running LLMs on this card, see our analysis of whether the RTX 4060 Ti can run LLaMA 3 8B or the RTX 4060 Ti DeepSeek guide. For the budget option, check if the RTX 3050 can run Stable Diffusion (SD 1.5 only). Browse all options on our dedicated GPU servers page or see the best GPU for Stable Diffusion guide.