How Much VRAM Do You Need for Flux? (2026 Guide)

From the Best GPU for AI archive. The canonical version has interactive calculators, an up-to-date GPU comparison table, and live pricing.

You need at least 12GB VRAM for Flux, and 16GB is what I actually recommend. Anything below 12GB requires aggressive offloading that makes generation painfully slow. The 8GB GPUs that work fine for SD 1.5 cannot handle Flux without serious compromises.

See the recommended pick on the original guide

Who this is for

You are planning to run Flux locally and want to know exactly how much VRAM each workflow requires before buying a GPU. Maybe you already have a card and want to know what Flux workflows it can handle.

VRAM requirements by workflow

These numbers assume FP16 inference with standard ComfyUI/A1111 settings. Quantized models (NF4/Q8) reduce usage by 30-50%.

VRAM chart available at the original article

See the recommended pick on the original guide

Why Flux uses more VRAM than SDXL

Flux uses a flow-matching transformer architecture that is fundamentally more memory-hungry than SDXL's UNet. The model weights alone are larger, and the attention mechanism scales with resolution. At 1024x1024, Flux dev holds about 12GB of state in memory versus 8GB for SDXL. For a comparison of SDXL vs Flux VRAM requirements, see our Stable Diffusion VRAM guide.

This is not something a driver update or optimization will fix. The architecture requires more memory. Period.

Which GPU should you buy?

• Just want to try Flux? An RTX 3060 12GB (~$250 used) runs Flux schnell and basic Flux dev. Slow, but functional.
• Serious Flux user? The RTX 4070 Ti Super (16GB, ~$700) is the sweet spot. It handles dev + LoRA with room to spare. The RTX 5070 Ti is also excellent at this price point.
• On the RTX 4060 Ti 16GB? It fits Flux Dev comfortably at 16GB — see our RTX 4060 Ti Flux walkthrough for exact VRAM usage and workflow limits.
• ControlNet + advanced workflows? You need 24GB. The RTX 4090 (~$1,600) or a used RTX 3090 (~$800) are your options.
• Fine-tuning Flux? The RTX 5090 (32GB, ~$2,000) gives you headroom for training without running out of memory.

Common mistakes to avoid

• Assuming 8GB is enough because it works for SD 1.5 -- Flux is a different architecture entirely. An RTX 4060 with 8GB cannot run Flux dev without model offloading that makes each image take over a minute.
• Ignoring quantization options -- NF4 Flux models reduce VRAM usage dramatically. A 16GB card can sometimes handle workflows that would normally need 24GB with quantized checkpoints. Our best quantization for Stable Diffusion guide covers which precision levels work best for different diffusion models.
• Buying for current Flux only -- Flux 2.0 and future flow-matching models will likely need even more VRAM. Buy one tier above your current minimum if your budget allows.

Final verdict

See the recommended pick on the original guide

See the recommended pick on the original guide

See the recommended pick on the original guide

For the full GPU ranking for Flux, check the best GPU for Flux guide. If you use ComfyUI specifically, the best GPU for ComfyUI article covers workflow-specific recommendations.

16GB is the new baseline for AI image generation. Flux proved that 8GB cards are no longer enough for cutting-edge models, and this trend will only continue.

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• Best GPU for Flux in 2026: 7 Cards Compared (From $249)
• How Much VRAM Do You Need for Stable Diffusion in 2026?
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Read the full guide on Best GPU for AI — includes our VRAM calculator, GPU comparison table, and live pricing.