What GPU do I need to run nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16?

123.6B parameters, published in BF16. View on Hugging Face

123.6B
Parameters
BF16
Native precision
NemotronHForCausalLM
Architecture
text-generation
Pipeline

NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16 is published by nvidia on Hugging Face, with 12,488 downloads and 32 likes to date. It's a NemotronHForCausalLM model built for text-generation, 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
230.2 GB
276.3 GB
A40 (runpod)
6
$2.64/hr
FP8 (quantized)
115.1 GB
138.1 GB
RTX 4080 Super (simplepod)
5
$1.90/hr
INT4 (quantized)
57.6 GB
69.1 GB
A100 (runpod)
1
$1.19/hr
cheaper alt.
RTX 4070 (simplepod)
6
$0.480/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 NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16 at its published (BF16) precision: 6× A40 on runpod, at $0.440/hr per GPU ($2.64/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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