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Open-weight model · Text generation

NVIDIA-Nemotron-3-Super-120B-A12B-BF16

by NVIDIA nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16

For more details on how to deploy and use the model - see the Quick Start Guide below! The post-training data has a cutoff date of February 2026. The pre-training data has a cutoff date of June 2025.

Parameters123.6B
Context262,144
Weights247.2 GB
Licenseother
AccessOpen weights
Monthly Downloads1.3M

Runs On

What it takes to serve NVIDIA-Nemotron-3-Super-120B-A12B-BF16 (123.6B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 247.2 GB 296.7 GB 2x MI300X (192 GB)
Vultr
$3.70 2x MI325X $4.00 · 2x MI355X $5.18
8-bit 123.6 GB 148.3 GB 1x MI300X (192 GB)
Vultr
$1.85 1x MI325X $2.00 · 1x MI355X $2.59
4-bit 61.8 GB 74.2 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

For more details on how to deploy and use the model - see the Quick Start Guide below! The post-training data has a cutoff date of February 2026. The pre-training data has a cutoff date of June 2025. NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents. Nemotron-3-Super-120B-A12B-BF16 is a large language model (LLM) trained by NVIDIA, designed to deliver strong agentic, reasoning, and conversational capabilities. It is optimized for collaborative agents and high-volume workloads such as IT ticket automation. Like other models in the family, it responds to user queries and…

Excerpt from the card by NVIDIA, licensed other.

Configuration

Architecture
NemotronHForCausalLM
Context length (tokens)
262,144
Layers
88
Hidden size
4,096
Feed-forward size
2,688
Attention heads
32
Key/value heads
2
Head dimension
128
Vocabulary size
131,072
Routed experts
512
Experts active per token
22
RoPE base
10,000
Model type
nemotron_h

Identity and Version

Repository
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
Publisher
NVIDIA
Task
Text generation
Modality
Text
Library
transformers
Parameters
123.6B parameters
Languages
en, fr, es, it, de, ja, zh
Revision
2dc98e2afe4face0e4ce40972a915c45368bd34a
First published
2026-03-10
Last updated
2026-08-25

Files and Weights

75 files, 247.2 GB in total. The weights are 50 files totalling 247.2 GB in safetensors.

Weights50 files · 247.2 GB
Configuration15 files · 4.3 MB
Tokenizer2 files · 17.3 MB
Documentation5 files · 93.2 KB
Other2 files · 89.9 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00050.safetensorsWeights5.0 GB 105bd2c4f20e
model-00002-of-00050.safetensorsWeights5.0 GB ec4d02dbe117
model-00003-of-00050.safetensorsWeights5.0 GB 137a7acc3c56
model-00004-of-00050.safetensorsWeights5.0 GB c0a198f937fa
model-00005-of-00050.safetensorsWeights5.0 GB 0e74a3aed4c6
model-00006-of-00050.safetensorsWeights5.0 GB 6ccac6d4cb59
model-00007-of-00050.safetensorsWeights5.0 GB 068306f784fb
model-00008-of-00050.safetensorsWeights5.0 GB a8f3b25f04a9
model-00009-of-00050.safetensorsWeights5.0 GB 1bc45e88a5fb
model-00010-of-00050.safetensorsWeights5.0 GB 3b9e6fe45335
model-00011-of-00050.safetensorsWeights5.0 GB ee3d39ef8833
model-00012-of-00050.safetensorsWeights5.0 GB 5ec01d95016c
model-00013-of-00050.safetensorsWeights5.0 GB 7aac706af7ea
model-00014-of-00050.safetensorsWeights5.0 GB eaa949d7ac1b
model-00015-of-00050.safetensorsWeights5.0 GB 0f16316eaa89
model-00016-of-00050.safetensorsWeights5.0 GB de2791b665ad
model-00017-of-00050.safetensorsWeights4.9 GB e8ea9d674374
model-00018-of-00050.safetensorsWeights5.0 GB 456272c3c51c
model-00019-of-00050.safetensorsWeights5.0 GB 1e5d3ceedf3a
model-00020-of-00050.safetensorsWeights5.0 GB 20bbcb0cc5b1
model-00021-of-00050.safetensorsWeights5.0 GB 53d503f49214
model-00022-of-00050.safetensorsWeights5.0 GB e92c37f824fd
model-00023-of-00050.safetensorsWeights4.9 GB faa4fcdb4a8b
model-00024-of-00050.safetensorsWeights5.0 GB 023e6b268664
model-00025-of-00050.safetensorsWeights5.0 GB ea00058bccb2
model-00026-of-00050.safetensorsWeights5.0 GB f4df92010a5d
model-00027-of-00050.safetensorsWeights5.0 GB 7bbe85c27447
model-00028-of-00050.safetensorsWeights5.0 GB 118a44204fe0
model-00029-of-00050.safetensorsWeights4.9 GB 2ac147a050c5
model-00030-of-00050.safetensorsWeights5.0 GB 24020dd8e7c8
model-00031-of-00050.safetensorsWeights5.0 GB 6f76e750d538
model-00032-of-00050.safetensorsWeights5.0 GB cafad8ebd189
model-00033-of-00050.safetensorsWeights5.0 GB 65496d854228
model-00034-of-00050.safetensorsWeights5.0 GB b1c4026a9de2
model-00035-of-00050.safetensorsWeights4.9 GB f926faa8ebb6
model-00036-of-00050.safetensorsWeights5.0 GB e4405f66cdcb
model-00037-of-00050.safetensorsWeights5.0 GB 0a2e63cdde42
model-00038-of-00050.safetensorsWeights5.0 GB 2a255376e905
model-00039-of-00050.safetensorsWeights5.0 GB b9d6aae1b6d1
model-00040-of-00050.safetensorsWeights5.0 GB cc9293969492
model-00041-of-00050.safetensorsWeights4.9 GB 305019d2d661
model-00042-of-00050.safetensorsWeights5.0 GB 5ab6b38d9b9b
model-00043-of-00050.safetensorsWeights5.0 GB 610d06cbd5be
model-00044-of-00050.safetensorsWeights5.0 GB a0c17be3f3ea
model-00045-of-00050.safetensorsWeights5.0 GB 60eb1cf2b2d5
model-00046-of-00050.safetensorsWeights5.0 GB 81feea7dde83
model-00047-of-00050.safetensorsWeights4.9 GB b3c105086bba
model-00048-of-00050.safetensorsWeights5.0 GB d48f4f962bc0
model-00049-of-00050.safetensorsWeights5.0 GB cb70fda7f2ea
model-00050-of-00050.safetensorsWeights2.9 GB 7f9b7cea8a18
.eval_results/gpqa.yamlConfiguration223 B
.eval_results/gpqa_with_tools.yamlConfiguration245 B
.eval_results/hle.yamlConfiguration212 B
.eval_results/hle_with_tools.yamlConfiguration234 B
.eval_results/mmlu_pro.yamlConfiguration227 B
.eval_results/swe_bench_verified.yamlConfiguration746 B
.eval_results/terminal-bench_2.0.yamlConfiguration250 B
__init__.pyConfiguration
config.jsonConfiguration1.9 KB
configuration_nemotron_h.pyConfiguration19.8 KB
generation_config.jsonConfiguration210 B
model.safetensors.index.jsonConfiguration4.2 MB
modeling_nemotron_h.pyConfiguration82.3 KB
special_tokens_map.jsonConfiguration563 B
super_v3_reasoning_parser.pyConfiguration1.9 KB
README.mdDocumentation82.6 KB
bias.mdDocumentation2.6 KB
explainability.mdDocumentation3.2 KB
privacy.mdDocumentation2.7 KB
safety.mdDocumentation2.1 KB
accuracy_chart.pngOther79.2 KB
chat_template.jinjaOther10.8 KB
.gitattributesRepository1.6 KB
tokenizer.jsonTokenizer17.1 MB 623c34567aeb
tokenizer_config.jsonTokenizer177.2 KB

License and Download

License
other
Access
Open weights, no gate
Download size
247.2 GB
Download from NVIDIA

Released by NVIDIA through its official repository on Hugging Face.

Built From

  • Described by arXiv:2512.20848
  • Described by arXiv:2512.20856
  • Trained on (disclosed) nvidia/nemotron-post-training-v3
  • Trained on (disclosed) nvidia/nemotron-pre-training-datasets

Evaluations

Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.

BenchmarkConditionsResultReported byRevisionDate
Idavidrein/gpqa Task diamondMetric diamondComparison conditions not established 79.23 Model Card
Reported by a third party
Evaluated revision not stated 2026-03-12
Idavidrein/gpqa Task diamondMetric diamondSetup With toolsComparison conditions not established 82.7 Model Card
Reported by a third party
Evaluated revision not stated 2026-03-12
MathArena/aime_2026 Task MathArena/aime_2026Metric MathArena/aime_2026Comparison conditions not established 90 Official MathArena Evaluation
Reported by a third party
Evaluated revision not stated 2026-03-17
MathArena/hmmt_feb_2026 Task MathArena/hmmt_feb_2026Metric MathArena/hmmt_feb_2026Comparison conditions not established 84.85 Official MathArena Evaluation
Reported by a third party
Evaluated revision not stated 2026-03-17
SWE-bench/SWE-bench_Multilingual Task swe_bench_multilingual_%_resolvedMetric swe_bench_multilingual_%_resolvedComparison conditions not established 45.8 Model Card
Reported by a third party
Evaluated revision not stated 2026-08-10
SWE-bench/SWE-bench_Verified Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup OpenCode harnessComparison conditions not established 59.2 Model Card
Reported by a third party
Evaluated revision not stated 2026-03-23
SWE-bench/SWE-bench_Verified Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup Codex harnessComparison conditions not established 53.73 Model Card
Reported by a third party
Evaluated revision not stated 2026-03-23
SWE-bench/SWE-bench_Verified Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup OpenHands harnessComparison conditions not established 60.47 Model Card
Reported by a third party
Evaluated revision not stated 2026-03-23
TIGER-Lab/MMLU-Pro Task mmlu_proMetric mmlu_proComparison conditions not established 83.73 Model Card
Reported by a third party
Evaluated revision not stated 2026-03-12
cais/hle Task hleMetric hleComparison conditions not established 18.26 Model Card
Reported by a third party
Evaluated revision not stated 2026-03-12
cais/hle Task hleMetric hleSetup With toolsComparison conditions not established 22.82 Model Card
Reported by a third party
Evaluated revision not stated 2026-03-12
claw-eval/Claw-Eval Task generalMetric generalSetup Pass³% | N=3 | 161 tasksComparison conditions not established 6.8 Claw-Eval Leaderboard
Reported by a third party
Evaluated revision not stated 2026-04-23
claw-eval/Claw-Eval Task multi_turnMetric multi_turnSetup Pass³% | N=3 | 38 tasksComparison conditions not established 0 Claw-Eval Leaderboard
Reported by a third party
Evaluated revision not stated 2026-04-23
harborframework/terminal-bench-2.0 Task terminalbench_2Metric terminalbench_2Comparison conditions not established 31 Model Card
Reported by a third party
Evaluated revision not stated 2026-03-12

Memory Requirements

PrecisionWeights in memory
As published247.2 GB
16-bit247.2 GB
8-bit123.6 GB
4-bit61.8 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Compare NVIDIA-Nemotron-3-Super-120B-A12B-BF16

Questions About NVIDIA-Nemotron-3-Super-120B-A12B-BF16

How much GPU memory does NVIDIA-Nemotron-3-Super-120B-A12B-BF16 need?

About 296.7 GB at 16-bit and 74.2 GB at 4-bit: the weights (123.6B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run NVIDIA-Nemotron-3-Super-120B-A12B-BF16 on?

At 16-bit, 2x MI300X from $3.70 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

What license is NVIDIA-Nemotron-3-Super-120B-A12B-BF16 released under?

other, as its publisher declares it. Read the license text before commercial use.

What is NVIDIA-Nemotron-3-Super-120B-A12B-BF16's context length?

262,144 tokens, from the maximum position embeddings in its published configuration.

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