NSFW AI VRAM Calculator 2026: Can My GPU Run It?

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Enter your GPU’s VRAM. We tell you which NSFW AI models you can actually run, and what you’ll need to compromise on. Based on Team AIGN’s hands-on tests on RTX 3060 12GB, RTX 4090 24GB, M2 Pro, and RunPod L40S.

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Need cloud GPU? If your local hardware is below 12 GB, the cheapest comfortable option is RunPod or Vast.ai for $0.30-0.80/hour. See our cost breakdown.

How we calibrated these numbers

VRAM thresholds come from Team AIGN’s owned hardware tests: RTX 4090 (24 GB), RTX 3060 (12 GB), Mac M2 Pro (32 GB unified), RunPod L40S (48 GB). Each model was tested at default settings, with –medvram, and with quantized weights where available. The “comfortable” threshold means you can run at recommended settings without offloading or memory tricks.

Frequently asked questions

Does shared GPU memory count?

No. The numbers above refer to dedicated VRAM only. Shared system RAM that Windows reports as “Shared GPU memory” causes severe slowdowns when used by AI workloads. Treat it as zero.

What about Apple Silicon unified memory?

M-series Macs share memory between CPU and GPU. For Stable Diffusion via DiffusionBee or Draw Things, treat 50% of unified RAM as available VRAM. A 32 GB M2 Pro effectively gives you 16 GB usable for SDXL.

Can I run Flux on 8 GB?

Technically with NF4 quantization yes, but quality and speed degrade significantly. We recommend 12 GB minimum for Flux Schnell and 16 GB for Flux Dev.

Why does SDXL need 8 GB when SD 1.5 only needs 4?

SDXL is roughly 3x the parameter count (3.5B vs 0.9B) and operates natively at 1024×1024 vs SD 1.5’s 512×512. Both factors compound the VRAM footprint.

What about RAM (system memory)?

16 GB system RAM is fine for any single-model workflow. 32 GB if you want to keep multiple checkpoints loaded for fast switching, or run ComfyUI with many custom nodes.

Related guides

See also: Rtx 3060 Vs 4060 Nsfw Ai 2026

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