Best Flux Model for 16GB VRAM (16GB VRAM)
Full-quality Flux.1 with LoRAs on a 16GB GPU
The best Flux setup for 16GB VRAM in 2026 is Flux.1 dev FP8 with a LoRA stack — about 13GB in ComfyUI and ~20 seconds per 1024px image on an RTX 4080. 16GB gives you full FP8 quality plus room for multiple LoRAs, ControlNet, or 1536px renders. You can also run GGUF Q8 for the highest fidelity short of the 24GB FP16 build.
Models that fit in 16GB VRAM
Tested reference: RTX 4080 / RTX 4060 Ti 16GB. Figures are for a 1024px image.
| Model | Size | Build | VRAM used | Speed |
|---|---|---|---|---|
| WINNERFlux.1 dev FP8 Full FP8 quality with headroom for LoRAs, ControlNet and higher resolution. ComfyUI FP8 checkpoint | 12B | FP8 | ~11GB (13GB w/ LoRAs) | ~20s / image |
| Flux.1 dev GGUF Q8 Highest fidelity below FP16 — closest to the reference model on 16GB. ComfyUI + GGUF loader | 12B | GGUF Q8 | ~13GB | ~24s / image |
| Flux.1 Schnell FP8 4-step, Apache-2.0 — near-instant drafts, batch generation. ComfyUI Schnell FP8 | 12B | FP8 | ~11GB | ~5s / image |
What won't fit in 16GB
- ✗Flux.1 dev FP16 (needs ~24GB) — The reference build still needs 24GB; FP8/Q8 on 16GB is visually near-identical.
How to fit more in 16GB
- →16GB is enough for FP8 + 2–3 LoRAs + ControlNet simultaneously — the practical creative ceiling before 24GB.
- →Use GGUF Q8 when you want the absolute best detail and can accept slightly slower generation.
- →Batch Schnell for ideation, then re-render the winners in dev FP8.
Quick start
git clone https://github.com/comfyanonymous/ComfyUIDrop flux1-dev-fp8.safetensors into models/checkpointsGo from “it runs” to actually building
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Start learning free →Frequently asked questions
Is 16GB VRAM enough for Flux?
Comfortably. Flux.1 dev FP8 uses ~11GB, leaving room for LoRAs, ControlNet and 1536px renders. 16GB is the practical sweet spot for creative Flux work short of a 24GB card.
Do I need 24GB for Flux?
Only for the full FP16 reference build or very heavy LoRA/ControlNet stacks. FP8 or GGUF Q8 on 16GB is visually near-identical for almost all prompts.
Can I train Flux LoRAs on 16GB?
Yes, with memory-efficient trainers (e.g. ai-toolkit / kohya with FP8 base and gradient checkpointing) — expect slow but workable training at 512–768px.
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