GPUs/AMD Radeon RX 6800 XT/Stable Diffusion 3.5 Large

Can AMD Radeon RX 6800 XT run Stable Diffusion 3.5 Large?

8B parameter Image Gen model on 16GB GDDR6

Yes — runs at 8-bit quantization
~2.5-3.4 img/min
SpeedFast inference, near-native speed
QualityNear-lossless — virtually identical to FP16
Stable Diffusion 3.5 Large works best with NVIDIA CUDA. Your AMD GPU may have limited or no support.

VRAM Requirements

Stable Diffusion 3.5 Large is a 8B parameter model. At full precision (FP16), it requires 18GB of VRAM. Your AMD Radeon RX 6800 XT has 16GB, so you'll need to quantize it to 8-bit (Q8) to fit.

FP16 (Full Precision)18GB (need 2GB more)

Maximum quality, no quantization

Q8 (8-bit)10GB (6GB free)

Near-lossless, ~50% size reduction

Q4 (4-bit)7GB (9GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 16GB GDDR6 at 512 GB/s bandwidth
Recommended system RAM: 32GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

Stable Diffusion 3.5 Large at 8-bit precision on AMD Radeon RX 6800 XT produces images virtually identical to full precision. Generation speed is fast and you'll have some VRAM headroom for larger batch sizes or higher resolutions.

How to Set It Up

Step 1: Install ComfyUI

git clone https://github.com/comfyanonymous/ComfyUI.git && cd ComfyUI && pip install -r requirements.txt

ComfyUI is the recommended UI for Stable Diffusion and FLUX models.

Step 2: Download the model

Download Stable Diffusion 3.5 Large weights from HuggingFace and place them in ComfyUI/models/. The model is approximately 18GB at full precision.

Step 3: Launch and generate

python main.py

Open http://localhost:8188 in your browser. You can use the full precision weights.

AMD Radeon RX 6800 XT Specs

VRAM16GB GDDR6
Memory Bandwidth512 GB/s
TDP300W
CUDA CoresN/A
Street Price~$320
AI Rating2/10

Other Image Gen Models on AMD Radeon RX 6800 XT

About Stable Diffusion 3.5 Large

Latest SD architecture. Better quality than SDXL, slightly more VRAM hungry.

Category: Image Gen · Parameters: 8B · CUDA required: Recommended