---
title: "SDXL 1.0 8 GB Low-VRAM Optimization in ComfyUI — Node Wiring & Settings"
description: "Complete ComfyUI workflow route for SDXL 1.0 paired with 8 GB Low-VRAM Optimization (running heavy weights on 8 GB RTX 3060 / 4060 cards without CUDA OOM). Loader: Load Checkpoint, VRAM target: 8 GB+."
canonical: https://comfy.ink/routes/sdxl-low-vram-8gb
updated: 2026-10-17
---

# SDXL 1.0: 8 GB Low-VRAM Optimization in ComfyUI

Blueprint for running heavy weights on 8 GB RTX 3060 / 4060 cards without CUDA OOM with SDXL 1.0 (8 GB+ VRAM) using FP8 / GGUF Q4 Loader → KSampler → VAE Decode (Tiled).

## Facts (2026-10-17)

- **Model Architecture:** SDXL 1.0
- **Workflow Task:** 8 GB Low-VRAM Optimization
- **VRAM Requirement:** 8 GB+
- **Primary Loader:** Load Checkpoint
- **Model Directory:** models/checkpoints/

## Node graph blueprint: SDXL 1.0 × 8 GB Low-VRAM Optimization

This route pairs **SDXL 1.0** with a **8 GB Low-VRAM Optimization** setup designed for running heavy weights on 8 GB RTX 3060 / 4060 cards without CUDA OOM. Place your model weights in `models/checkpoints/` and start the graph with **Load Checkpoint**.

Core wiring chain: `FP8 / GGUF Q4 Loader → KSampler → VAE Decode (Tiled)`. Connect your positive and negative conditioning from [CLIP Text Encode](https://comfy.ink/nodes/clip-text-encode) into [KSampler](https://comfy.ink/nodes/ksampler) and decode to RGB pixels.

## VRAM tuning and execution tips on Windows

- **Memory target (8 GB+):** If your GPU sits at 8 GB or 12 GB, use FP8 or GGUF Q4/Q5 weights and swap in [VAE Decode (Tiled)](https://comfy.ink/nodes/vae-decode-encode) to avoid end-of-pass memory spikes.
- **Workflow persistence:** Every image saved by this route embeds the full JSON graph inside the PNG metadata — drag the output back onto [ComfyUI Studio](https://comfy.ink/studio) anytime to resume.
- **Get the app:** Download the Windows desktop or portable build from [/download](https://comfy.ink/download).

## FAQ

### Which loader node do I use for SDXL 1.0 in a 8 GB Low-VRAM Optimization?

Use `Load Checkpoint` with weights stored in `models/checkpoints/`, then wire the output into `FP8 / GGUF Q4 Loader → KSampler → VAE Decode (Tiled)`.

### How much VRAM does SDXL 1.0 need for 8 GB Low-VRAM Optimization?

Target VRAM is 8 GB+. On 8 GB cards, load quantized FP8 or GGUF weights and enable tiled VAE decoding.

## Related

- [ComfyUI Model & Task Routes](https://comfy.ink/routes)
- [ComfyUI Models & Checkpoints](https://comfy.ink/models)
- [KSampler & KSampler (Advanced)](https://comfy.ink/nodes/ksampler)
- [ComfyUI VRAM Requirements for SDXL, Flux & Video (6GB–24GB)](https://comfy.ink/guides/comfyui-vram-requirements-guide)
- [Flux.1: Text-to-Image Graph in ComfyUI](https://comfy.ink/routes/flux-txt2img-workflow)

## Download

Free desktop app: [Download Comfy](https://comfy.ink/download)

Markdown mirror: https://comfy.ink/routes/sdxl-low-vram-8gb.md