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NVIDIA-Nemotron-3-Super-120B-A12B-BF16 GPU Requirements: VRAM & Cheapest GPU

NVIDIA-Nemotron-3-Super-120B-A12B-BF16 has about 124B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.

124BParameters
67 GBMin VRAM
$0.82/hrCheapest
< 2 minDeploy
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
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124B paramstext-generationnemotron_h778.9K downloads377 likesupdated Apr 29, 2026

To run NVIDIA-Nemotron-3-Super-120B-A12B-BF16 for inference at FP16, you need roughly 269 GB of VRAM. The cheapest fit on Spheron is 4x A100 80GB at about $3.28/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.

GB VRAM REQUIRED
FP16INFERENCEBATCH 1CTX 4k

Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.

RANKCONFIGURATIONPER GPUTOTAL $/HR
  • 01
    4× A100 80GBCHEAPEST
    Ampere · HBM2e
    $0.82/hr$3.28/hr
  • 02
    4× RTX PRO 6000 96GB
    Blackwell · GDDR7
    $1.32/hr$5.28/hr
  • 03
    2× H200 141GB
    Hopper · HBM3e
    $3.31/hr$6.62/hr
  • 04
    4× GH200 96GB
    Grace Hopper · HBM3
    $1.88/hr$7.52/hr
  • 05
    8× L40S 48GB
    Ada Lovelace · GDDR6
    $0.96/hr$7.68/hr

Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.

VRAM required to run NVIDIA-Nemotron-3-Super-120B-A12B-BF16

Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.

PrecisionInferenceLoRA fine-tuneFull fine-tune
FP16269 GB404 GB1078 GB
INT8135 GB202 GB539 GB
INT467 GB101 GB269 GB

Cheapest GPU to run NVIDIA-Nemotron-3-Super-120B-A12B-BF16 by precision

FP16
VRAM required269GB

Full precision. Best quality, highest memory.

Cheapest GPU
4x A100 80GB
Ampere · HBM2e
$3.28/hr · $0.82/hr/gpu
4x A100 80GB on Spheron
INT8
VRAM required135GB

8-bit quantized. ~2x smaller, minimal quality loss.

Cheapest GPU
2x A100 80GB
Ampere · HBM2e
$1.64/hr · $0.82/hr/gpu
2x A100 80GB on Spheron
INT4
VRAM required67GB

4-bit quantized. ~4x smaller, runs on smaller GPUs.

Cheapest GPU
A100 80GB
Ampere · HBM2e
$0.82/hr
A100 80GB on Spheron

Inference vs fine-tuning NVIDIA-Nemotron-3-Super-120B-A12B-BF16

InferenceWeights + KV cache
LoRA fine-tune~1.5×+ low-rank adapter
Full fine-tune~4×+ gradients + optimizer state

Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. For NVIDIA-Nemotron-3-Super-120B-A12B-BF16, an on-demand A100 80GB instance covers inference and LoRA, while a full fine-tune needs several times that memory and often spans multiple GPUs. Check the live GPU pricing for current rates.

Deployment guideDeploy Nemotron 3 Super step by stepHands-on production setup, GPU configs, and benchmarks for NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Read guide

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