What GPU do I need to run nvidia/Cosmos-Reason2-2B?

2.4B parameters, published in BF16. View on Hugging FaceGated

2.4B
Parameters
BF16
Native precision
Qwen3VLForConditionalGeneration
Architecture
image-text-to-text
Pipeline

Cosmos-Reason2-2B is published by nvidia on Hugging Face, with 982,194 downloads and 174 likes to date. It's a Qwen3VLForConditionalGeneration model built for image-text-to-text, 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
4.5 GB
5.5 GB
RTX 3070 (simplepod)
1
$0.050/hr
FP8 (quantized)
2.3 GB
2.7 GB
RTX 4070 (simplepod)
1
$0.080/hr
INT4 (quantized)
1.1 GB
1.4 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 Cosmos-Reason2-2B at its published (BF16) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/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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