What GPU do I need to run black-forest-labs/FLUX.2-dev?

32.2B parameters, published in BF16. View on Hugging FaceGated

Set up FLUX.2-dev
32.2B
Parameters
BF16
Native precision
Unknown
Architecture
image-to-image
Pipeline

FLUX.2-dev is published by black-forest-labs on Hugging Face, with 608,892 downloads and 2,139 likes to date. It's a unlisted-architecture model built for image-to-image, published natively in BF16, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.

VRAM required & cheapest live GPU fit

Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.

Precision
Weight size
Required VRAM
Cheapest live fit
GPUs needed
Est. $/hr (full fit)
BF16
60.0 GB
72.0 GB
A100 (runpod)
1
$1.19/hr
cheaper alt.
RTX 3060 (simplepod)
7
$0.560/hr
FP8 (quantized)
30.0 GB
36.0 GB
L40 (runpod)
1
$0.690/hr
cheaper alt.
RTX 4000 Ada (runpod)
2
$0.400/hr
INT4 (quantized)
15.0 GB
18.0 GB
RTX 3090 (simplepod)
1
$0.160/hr
cheaper alt.
RTX 3080 (simplepod)
2
$0.140/hr

A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.

INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.

Cheapest way to run FLUX.2-dev at its published (BF16) precision: 1× A100 on runpod, at $1.19/hr per GPU ($1.19/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.

Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.

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