What GPU do I need to run allenai/Molmo2-4B?

4.9B parameters, published in F32. View on Hugging Face

4.9B
Parameters
F32
Native precision
Molmo2ForConditionalGeneration
Architecture
image-text-to-text
Pipeline

Molmo2-4B is published by allenai on Hugging Face, with 100,023 downloads and 54 likes to date. It's a Molmo2ForConditionalGeneration model built for image-text-to-text, published natively in F32.

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)
FP32
18.1 GB
21.7 GB
P40 (akash)
1
$0.137/hr
cheaper alt.
P4 (akash)
3
$0.095/hr
FP8 (quantized)
4.5 GB
5.4 GB
RTX 4070 (simplepod)
1
$0.080/hr
INT4 (quantized)
2.3 GB
2.7 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 Molmo2-4B at its published (F32) precision: 1× P40 on akash, at $0.137/hr per GPU ($0.137/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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