Flux image · 8GB VRAM

Best Flux Model for 8GB VRAM (8GB VRAM)

Running Flux.1 on an 8GB GPU with quantized builds

📅 Published: July 7, 2026🔄 Last Updated: July 2026✓ Manually Reviewed

Short answer

The best Flux model for 8GB VRAM in 2026 is Flux.1 dev GGUF Q4 — about 7GB in ComfyUI and roughly 60 seconds per 1024px image on an RTX 4060. If you want speed over fidelity, Flux.1 Schnell (4-step) is faster, and the NF4 build is the lightest. The full FP16 Flux (~24GB) will not fit 8GB — you must run a quantized (GGUF/NF4) build.

Models that fit in 8GB VRAM

Tested reference: RTX 4060 / RTX 3070 (8GB). Figures are for a 1024px image.

Model	Size	Build	VRAM used	Speed
WINNERFlux.1 dev GGUF Q4 Best quality that fits 8GB — use the ComfyUI GGUF loader; keep resolution at 1024px. `ComfyUI + city96 GGUF loader`	12B	GGUF Q4	~7GB	~60s / image
Flux.1 dev NF4 Lightest dev build — a touch lower fidelity than GGUF Q4 but leaves headroom. `Forge / ComfyUI NF4 build`	12B	NF4	~6GB	~55s / image
Flux.1 Schnell (GGUF Q4) Apache-2.0, 4-step — far faster, lower detail; great for drafts and iteration. `ComfyUI + Schnell GGUF`	12B	GGUF Q4	~7GB	~12s / image

What won't fit in 8GB

✗Flux.1 dev FP16 (needs ~24GB) — Full precision — needs a 24GB card.
✗Flux.1 dev FP8 (needs ~12GB) — Needs 12GB — see the 12GB guide.

How to fit more in 8GB

→Use ComfyUI with the GGUF loader — it streams the model and uses far less VRAM than Diffusers.
→Move the T5 text encoder to CPU (––lowvram or the ComfyUI "clip on CPU" node) to free ~2GB of VRAM.
→Stay at 1024×1024 — Flux VRAM scales with resolution; 1536px will OOM an 8GB card.

Quick start

Get ComfyUI

git clone https://github.com/comfyanonymous/ComfyUI

Add the GGUF loader

ComfyUI-Manager → install "ComfyUI-GGUF"

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Frequently asked questions

Can Flux run on 8GB VRAM?

Yes — not the full FP16 model, but the GGUF Q4 or NF4 build of Flux.1 dev runs in ~6–7GB in ComfyUI at ~55–60s per 1024px image. Move the text encoder to CPU to free more VRAM.

Flux.1 dev vs Schnell on 8GB?

Dev gives better detail (~60s/image); Schnell is 4-step and ~12s/image with lower fidelity and a permissive Apache-2.0 licence. Use Schnell to iterate, dev for finals.

Why won’t full Flux fit 8GB?

Flux.1 is a 12B-parameter model — at FP16 the weights alone are ~24GB. Quantized GGUF/NF4 builds compress that to ~6–7GB, which is why they are the only option on 8GB.

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