What GPU do I need to run stabilityai/stable-diffusion-3-medium-diffusers?

2.1B parameters, published in F16. View on Hugging FaceGated

2.1B
Parameters
F16
Native precision
Unknown
Architecture
text-to-image
Pipeline

stable-diffusion-3-medium-diffusers is published by stabilityai on Hugging Face, with 38,197 downloads and 465 likes to date. It's a unlisted-architecture model built for text-to-image, published natively in F16, 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 activation memory and allocator fragmentation. Diffusion and video models carry no KV-cache — the real driver of extra memory is output resolution and frame count, which this flat overhead does not model. Full formula and assumptions: methodology.

Precision
Weight size
Required VRAM
Cheapest live fit
GPUs needed
Est. $/hr (full fit)
FP16
3.9 GB
4.7 GB
P4 (akash)
1
$0.032/hr
FP8 (quantized)
1.9 GB
2.3 GB
RTX 4070 Super (simplepod)
1
$0.090/hr
INT4 (quantized)
1.0 GB
1.2 GB
RTX 3070 (simplepod)
1
$0.050/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 caveat: requires a quantized checkpoint actually published for this model — check its Hugging Face page before relying on this row.

Cheapest way to run stable-diffusion-3-medium-diffusers at its published (F16) precision: 1× P4 on akash, at $0.032/hr per GPU ($0.032/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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