LLM
Nemotron 3 Super
NVIDIA's 120B hybrid MoE model; base for Nemotron-SEA-LION v4.8.
Levelmodel
ClassLLM
Parameters120B
Context262,000
Released2026
Opennessopen source
Runs onsingle datacentre GPU
Statusactive
Benchmark Results
maintainer-run
SEA-HELMSoutheast Asian and Indonesian
maintainer-run
70.78 95% CI [-1.22, +1.20]
04080
Score in score. Higher is better.
rank 8, SEA-HELM Indonesian, open-weight models, average of 2,000 bootstraps
maintainer-run
49.3 95% CI [-0.51, +0.53]
04080
Score in score. Higher is better.
SEA-HELM SEA average, open-weight models
Where this comes from
Every score names who ran it: vendor-reported, maintainer-run or independent. A rank is shown with the population it was ranked in (gap rule 3).
Lineage
finetune
Nemotron-SEA-LION-v4.8-120B-A12B is a child (finetune)high confidencemodel cardWhere this comes from
Parent and child relations from model cards, Hugging Face base_model fields and the genealogy tables of the research notes. The tree shows two generations each way, 2 models. Confidence and evidence are the notes' own.
R&D Topics
Identity
- Developer
- NVIDIA
- Family
- Nemotron 3
- Released
- 2026 sourcesregistry date
- Weights published
- –
- Record checked
- 10 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- hybrid Hybrid Mamba-Transformer MoE
- Total parameters
- 120B sources
- Active parameters
- 12B
- Checkpoint parameters
- –
- Context length
- 262,000 tokensunverified
- Input
- text
- Output
- text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- open source
- Licence
- OpenMDW License v1.1 sources
- Access routes
- open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- single datacentre GPU
- Footprint
- Single GPU at NVFP4 (listed at 67B)
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- measured on a benchmark
- SEA-HELM Indonesian leaderboard (ID aggregate, first score column of the Indonesian Competency and Task Scores table): 70.78 (95% CI -1.22 to +1.20), run by AI Singapore (SEA-HELM leaderboard maintainer) leaderboard.sea-lion.ai
- Regional languages
- not in the notes
- Jakarta hosting
- in-region Jakarta (Amazon Bedrock (ap-southeast-3 Jakarta))
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Industry Use Cases
No industry use case links this model.
Links
DeveloperNVIDIAbig tech, USA
NVIDIAbig tech, USAModelNemotron 3 SuperNVIDIA, LLM
Nemotron 3 SuperNVIDIA, LLMR&D TopicsEfficiency Architectures (MoE, Sparse Attention, Long Context)Low-Resource and Regional Language ResourcesOpen-Weight Frontier Releases as Research SubstrateRegional or National Foundation Model Training (Continued Pretraining at 7B to 70B)
Use CasesIndonesian and Regional-Language LLMsPublic SectorIndonesian NLU (Classification, NER)Public SectorBanking Customer Service AssistantsFinanceCitizen Service AssistantsPublic Sector+8 moreUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026
PT Bank Tabungan Negara (BTN)In production, 1 Jul 2026
PT PegadaianIn production, 5 Jun 2026
Bank Central Asia (BCA)In production, 16 Mar 2026+19 moreDeployments of these use cases; none names this model.
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026PT Bank Tabungan Negara (BTN)In production, 1 Jul 2026PT PegadaianIn production, 5 Jun 2026Bank Central Asia (BCA)In production, 16 Mar 2026+19 moreDeployments of these use cases; none names this model.Organisations 1
R&D topics 4
Open-Weight Frontier Releases as Research SubstrateUsing released weights as the base for downstream research.used byEfficiency Architectures (MoE, Sparse Attention, Long Context)Reducing training and inference cost per capability.produced byRegional or National Foundation Model Training (Continued Pretraining at 7B to 70B)Pretraining or continued pretraining a mid-size model to serve a national or regional lan…used byLow-Resource and Regional Language ResourcesDatasets, benchmarks and adaptation for under-served languages.used by
Sources
- Open-Source LLMs 2026: Which Are Actually Open-Licensed codersera.com https://codersera.com/blog/open-source-llms-landscape-2026/
- huggingface.co/nvidia https://huggingface.co/nvidia
- huggingface.co/nvidia/models?search=nemotron-3 https://huggingface.co/nvidia/models?search=nemotron-3
- SEA-HELM Leaderboard leaderboard.sea-lion.ai/detailed/SEA https://leaderboard.sea-lion.ai/detailed/SEA
- SEA-HELM Leaderboard leaderboard.sea-lion.ai/detailed/ID https://leaderboard.sea-lion.ai/detailed/ID
- docs.aws.amazon.com/bedrock/latest/userguide/models-region-compat... https://docs.aws.amazon.com/bedrock/latest/userguide/models-region-compatibility.md
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Nemotron 3 Super", https://aurion.semai.id/models/nvidia-nemotron-3-super/, records of October 2026.