What GPU do I need to run nvidia/Cosmos3-Super-Image2Video?

64.6B parameters, published in BF16. View on Hugging Face

64.6B
Parameters
BF16
Native precision
Cosmos3ForConditionalGeneration
Architecture
image-to-video
Pipeline

Cosmos3-Super-Image2Video is published by nvidia on Hugging Face, with 66,223 downloads and 150 likes to date. It's a Cosmos3ForConditionalGeneration model built for image-to-video, published natively in BF16.

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)
BF16
120.3 GB
144.4 GB
RTX 3090 (simplepod)
7
$1.12/hr
FP8 (quantized)
60.2 GB
72.2 GB
1
$1.16/hr
cheaper alt.
RTX 5060 Ti (simplepod)
5
$0.500/hr
INT4 (quantized)
30.1 GB
36.1 GB
A40 (runpod)
1
$0.440/hr
cheaper alt.
RTX 6000 (akash)
2
$0.231/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 Cosmos3-Super-Image2Video at its published (BF16) precision: 7× RTX 3090 on simplepod, at $0.160/hr per GPU ($1.12/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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