Flux.1: 8 GB Low-VRAM Optimization in ComfyUI
Blueprint for running heavy weights on 8 GB RTX 3060 / 4060 cards without CUDA OOM with Flux.1 (8 GB–16 GB VRAM) using FP8 / GGUF Q4 Loader → KSampler → VAE Decode (Tiled).
Updated Oct 17, 2026 · 1 min read
- Model Architecture
- Flux.1
- Workflow Task
- 8 GB Low-VRAM Optimization
- VRAM Requirement
- 8 GB–16 GB
- Primary Loader
- Load Diffusion Model + DualCLIPLoader
- Model Directory
- models/unet/
Node graph blueprint: Flux.1 × 8 GB Low-VRAM Optimization
This route pairs **Flux.1** 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/unet/` and start the graph with **Load Diffusion Model + DualCLIPLoader**.
Core wiring chain: `FP8 / GGUF Q4 Loader → KSampler → VAE Decode (Tiled)`. Connect your positive and negative conditioning from CLIP Text Encode into KSampler and decode to RGB pixels.
VRAM tuning and execution tips on Windows
- **Memory target (8 GB–16 GB):** If your GPU sits at 8 GB or 12 GB, use FP8 or GGUF Q4/Q5 weights and swap in VAE Decode (Tiled) 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 anytime to resume.
- **Get the app:** Download the Windows desktop or portable build from /download.
FAQs
Which loader node do I use for Flux.1 in a 8 GB Low-VRAM Optimization?
Use `Load Diffusion Model + DualCLIPLoader` with weights stored in `models/unet/`, then wire the output into `FP8 / GGUF Q4 Loader → KSampler → VAE Decode (Tiled)`.
How much VRAM does Flux.1 need for 8 GB Low-VRAM Optimization?
Target VRAM is 8 GB–16 GB. On 8 GB cards, load quantized FP8 or GGUF weights and enable tiled VAE decoding.
Run on your hardware. Free forever.
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