What GPU do I need to run meta-models/Muse-Glimmer-30B?

29.8B parameters, published in BF16. View on Hugging Face

29.8B
Parameters
BF16
Native precision
MuseGlimmerForConditionalGeneration
Architecture
image-text-to-text
Pipeline

Muse-Glimmer-30B is published by meta-models on Hugging Face, with 609,919 downloads and 1,824 likes to date. It's a MuseGlimmerForConditionalGeneration model built for image-text-to-text, published natively in BF16.

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
55.5 GB
66.6 GB
A100 (runpod)
1
$1.19/hr
cheaper alt.
RTX 4070 (simplepod)
6
$0.480/hr
FP8 (quantized)
27.7 GB
33.3 GB
L40 (massecompute)
1
$0.772/hr
cheaper alt.
RTX 4070 (simplepod)
3
$0.240/hr
INT4 (quantized)
13.9 GB
16.6 GB
RTX 3090 (simplepod)
1
$0.160/hr
cheaper alt.
RTX 3080 (simplepod)
2
$0.140/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 Muse-Glimmer-30B at its published (BF16) precision: 1× A100 on runpod, at $1.19/hr per GPU ($1.19/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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