What GPU do I need to run allenai/Molmo-7B-D-0924?

8.0B parameters, published in F32. View on Hugging Face

8.0B
Parameters
F32
Native precision
MolmoForCausalLM
Architecture
image-text-to-text
Pipeline

Molmo-7B-D-0924 is published by allenai on Hugging Face, with 23,630 downloads and 566 likes to date. It's a MolmoForCausalLM 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
29.9 GB
35.9 GB
RTX 8000 (akash)
1
$0.221/hr
cheaper alt.
P4 (akash)
5
$0.158/hr
FP8 (quantized)
7.5 GB
9.0 GB
RTX 4070 (simplepod)
1
$0.080/hr
INT4 (quantized)
3.7 GB
4.5 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 Molmo-7B-D-0924 at its published (F32) precision: 1× RTX 8000 on akash, at $0.221/hr per GPU ($0.221/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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