Can RTX 3050 Run Stable Diffusion?

Yes, the RTX 3050 can run Stable Diffusion 1.5 at 512×512 resolution, but its 6GB VRAM severely limits what you can do with newer models like SDXL. If you are looking at RTX 3050 hosting for image generation, you need to understand exactly where the VRAM ceiling hits. For reliable Stable Diffusion hosting, the model version and resolution matter enormously on a 6GB card.

The Short Answer

YES for SD 1.5 at 512×512. NO for SDXL at 1024×1024.

The RTX 3050 ships with 6GB GDDR6 VRAM. Stable Diffusion 1.5 with its UNet weights in FP16 needs roughly 2GB of VRAM for the model alone, leaving headroom for 512×512 generation. However, SDXL requires approximately 6.5GB just for the base model in FP16, which already exceeds the 3050’s total VRAM before accounting for the latent tensors and VAE decoder. You will hit out-of-memory errors immediately at default settings.

With aggressive optimisations like --medvram-sdxl in Automatic1111 or using Torch attention slicing, you might squeeze SDXL at 512×512 but generation times become impractical. For production Stable Diffusion workloads, 6GB is the bare minimum for SD 1.5 only.

VRAM Analysis

Here is how each Stable Diffusion variant stacks up against the RTX 3050’s 6GB VRAM budget:

The key takeaway is that SD 1.5 at its native 512×512 fits comfortably, but the moment you move to SDXL or higher resolutions the 6GB wall becomes a hard blocker. Check our best GPU for Stable Diffusion guide for a full breakdown across all cards.

Performance Benchmarks

Benchmark data for Stable Diffusion 1.5 at 512×512, 20 steps, Euler sampler, batch size 1:

At around 4.2 iterations per second, the RTX 3050 is usable for personal experimentation but falls short for any production pipeline. Generating a single 512×512 image takes close to 5 seconds, which adds up quickly for batch workloads. You can compare detailed throughput numbers on our benchmarks page.

Setup Guide

To get Stable Diffusion running on an RTX 3050 server, the easiest path is ComfyUI or Automatic1111 with memory optimisations enabled:

# Clone and launch Automatic1111 with low-VRAM flags
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui
python launch.py --medvram --xformers --listen --port 7860

The --medvram flag moves model components between GPU and CPU as needed, keeping peak VRAM usage below 6GB. The --xformers flag enables memory-efficient attention which reduces VRAM usage further and improves speed by roughly 10-15%.

For ComfyUI, which is generally more memory efficient:

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

Stick to SD 1.5 checkpoints and avoid loading multiple models simultaneously. LoRA models add minimal VRAM overhead and work well on this card.

Recommended Alternative

If you need SDXL support or plan to run higher resolutions, the RTX 3050 will hold you back. The RTX 4060 Ti with 16GB VRAM is the sweet spot for Stable Diffusion work. It runs SDXL at 1024×1024 natively in FP16, handles the refiner model with offloading, and delivers more than double the iteration speed. Read our comparison of whether the RTX 4060 Ti can run SDXL for the full picture.

For budget-conscious setups where SD 1.5 is sufficient, the RTX 4060 with 8GB offers better performance at a modest price increase. If you want headroom for future models including Flux.1, the RTX 3090 with 24GB remains an excellent value option. Check whether the RTX 3050 can handle DeepSeek if you also need LLM capabilities on the same card.