Can my PC run Stable Diffusion, Flux and Wan?

Can I run FLUX.2 dev? VRAM requirements and speed

FLUX.2 dev has 32B parameters and needs about 67 GB of graphics memory at full precision (FP16), with its working memory for a 1024 × 1024 image; about 35 GB at FP8; and about 23 GB as a GGUF Q4_K_M file, so it runs entirely on a 24 GB card or bigger.

Check your graphics card

Memory at each precision

The model file and the working memory it needs on the card, with about 0.8 GB more kept for the driver and the screen. 8-bit (FP8 or Q8_0) looks almost the same as full precision; GGUF Q4 loses some fine detail.

PrecisionModelOn the cardFits a card with
FP1664.0 GB67.0 GB80 GB
FP832.0 GB35.0 GB48 GB
GGUF Q8_034.0 GB37.0 GB48 GB
GGUF Q6_K26.2 GB29.2 GB32 GB
GGUF Q5_K_M22.7 GB25.7 GB32 GB
GGUF Q4_K_M19.5 GB22.5 GB24 GB

The text encoder, a 24B language model (Mistral Small 3.1), reads your prompt first, about 18 GB at 8 bits; ComfyUI moves it off the card before drawing, so it needs system memory rather than graphics memory.

Its text encoder alone is a 24B language model, about 18 GB at 8 bits, so it needs plenty of system memory.

On common graphics cards

Time for a 1024 × 1024 image at 28 steps, at the largest precision that fits, in a PC with 32 GB of system memory.

Graphics cardRuns?PrecisionTime
RTX 3060 12 GBWon’t fit——
RTX 4060 (8 GB)Won’t fit——
RTX 4060 Ti 16 GBWon’t fit——
RTX 5060 Ti 16 GBWon’t fit——
RTX 4070 (12 GB)Won’t fit——
RTX 5070 (12 GB)Won’t fit——
RTX 4070 Ti SUPER (16 GB)Won’t fit——
RTX 5070 Ti (16 GB)Won’t fit——
RTX 3090 (24 GB)On the cardGGUF Q4_K_M75 s–3 min
RTX 4080 SUPER (16 GB)Won’t fit——
RTX 5080 (16 GB)Won’t fit——
RTX 4090 (24 GB)On the cardGGUF Q4_K_M32 s–65 s
RTX 5090 (32 GB)On the cardGGUF Q6_K28 s–55 s
Radeon RX 9070 XT (16 GB)Won’t fit——
Radeon RX 7900 XTX (24 GB)On the cardGGUF Q4_K_M—
Arc B580 (12 GB)Won’t fit——

AMD and Intel cards: whether it fits, but no time, which depends too much on the software.

About FLUX.2 dev

Made byBlack Forest Labs
ReleasedNovember 2025
Makes1024 × 1024 images
Parameters32B
LicenceFLUX.2 [dev] Non-Commercial License

Its files are on Hugging Face. ComfyUI runs it with a ready-made workflow; GGUF files need the ComfyUI-GGUF add-on. Bottleneck is not connected with Black Forest Labs.

How it is worked out

Image and video models are limited by the graphics card’s arithmetic rather than its memory speed, so each card’s speed here comes from its measured arithmetic in llama.cpp’s benchmark (the same figure the AI model calculator uses for reading prompts), and each model’s time per step is anchored to measured times: FLUX.1 dev at about 1.85 steps a second on an RTX 4090, SDXL at about 4.2 a second on an RTX 3090, and Wan 2.2 5B taking about nine minutes for a 720p clip on an RTX 4090, its makers’ own figure. Other models scale from those by size. Memory is the model at each precision plus its working memory at the usual resolution, with about 0.8 GB kept for the driver and the screen. Speeds are for NVIDIA cards; AMD and Intel cards run these models through ROCm, ZLUDA, DirectML or Intel’s own builds, whose speed varies too much with the software to estimate. A real time is usually within the range shown; speed-up add-ons such as fewer-step LoRAs, TensorRT or SageAttention make it faster.

See what is holding your graphics card back

Bottleneck’s free app reads your graphics card, how much of its memory other programs are holding, your memory’s speed and channels, and the settings that slow a PC down, and says how to fix each. Its AI models screen lists the chat models your PC runs, and checks one you already run with Ollama or LM Studio.

The free version gives each check’s verdict, with no time limit and no account. Pro, $9 once, explains the cause and the fix. Compare the versions