Model reference · open weights

NVIDIA-Nemotron-3-Super

LLMs nvidia Text gen · MoE 3 builds Its own licence terms 1.1M dl/mo

NVIDIA-Nemotron-3-Super is an open-weight language model from NVIDIA. NVIDIA-Nemotron-3-Super-120B-A12B-FP8 (FP8) weighs 128 GB; the smallest configuration that runs it is 2× A100 80 GB.

  • NVIDIA-Nemotron-3-Super is a text-generation model developed by NVIDIA, designed for agentic workflows, long-context reasoning, and high-volume workloads.
  • It features a hybrid LatentMoE architecture with 120B total parameters and 12B active parameters, supporting a context length of up to 1M tokens.
  • The model supports English, French, Spanish, Italian, German, Japanese, and Chinese, and is released under the NVIDIA Nemotron Open Model License.

Summary of the nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 model card, 2026-10-01. The estimate below is for another build of the family.

What it is

Released byNVIDIA
Released2026-03-10
Parameters67.2B
VRAM128 GB for the weights

What it runs on

Memory and cards for NVIDIA-Nemotron-3-Super-120B-A12B-FP8 (FP8)

128 GBweights, file size
4 MBcache per 1K tokens (8-bit)
340 MBfixed state per request
4.9 GBruntime overhead, at least
262,144 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … H200 141 GB
11 smaller cards
———
B200 180 GB2318all 256K176 GB
2× A100 80 GB
tensor parallel · FP8 without its speed-up here
4838all 256K78.2 GB a card
4× L40S 48 GB
tensor parallel
6845all 256K44.0 GB a card
2× RTX PRO 6000 Blackwell 96 GB
tensor parallel
4333all 256K93.8 GB a card
2× H200 141 GB
tensor parallel
279219all 256K138 GB a card
4× H100 80 GB
tensor parallel
239160all 256K78.1 GB a card
2× B200 180 GB
tensor parallel
390307all 256K176 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
1134 GB134 GB
5135 GB136 GB
8136 GB137 GB
16139 GB141 GB
32145 GB148 GB
64157 GB164 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 ↗ 67.2BNVFP4 80.3 GBDGX Spark (GB10) 128 GB unified
NVIDIA-Nemotron-3-Super-120B-A12B-BF16 ↗ 123.6BBF16 247 GB4× A100 80 GB
NVIDIA-Nemotron-3-Super-120B-A12B-FP8 (above) ↗ 123.6BFP8 128 GB2× A100 80 GB

From the model card

What NVIDIA says about NVIDIA-Nemotron-3-Super

Read the model card

Model Summary

Total Parameters120B (12B active)
ArchitectureLatentMoE - Mamba-2 + MoE + Attention hybrid with Multi-Token Prediction (MTP)
Context LengthUp to 1M tokens
Minimum GPU Requirement1× B200 OR 1× DGX Spark
Supported LanguagesEnglish, French, German, Italian, Japanese, Spanish, Chinese
Best ForAgentic workflows, long-context reasoning, high-volume workloads (e.g. IT ticket automation), tool use, RAG
Reasoning ModeConfigurable on/off via chat template (enable_thinking=True/False)
Speculative DecodingIncludes a built-in MTP head, with an updated MTPv2 head available as a separate checkpoint
LicenseNVIDIA Nemotron Open Model License
Release DateMarch 11, 2026

Quick Start

Use temperature=1.0 and top_p=0.95 across all tasks and serving backends — reasoning, tool calling, and general chat alike.

For more details on how to deploy and use the model - see the Quick Start Guide below!

Model Overview

Model Developer: NVIDIA Corporation

Model Dates: December 2025 - March 2026

Data Freshness:

  • The post-training data has a cutoff date of February 2026.
  • The pre-training data has a cutoff date of June 2025.

What is Nemotron?

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.

Description

Nemotron-3-Super-120B-A12B-NVFP4 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 tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be configured through a flag in the chat template.

The model employs a hybrid Latent Mixture-of-Experts (LatentMoE) architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. Distinct from the Nano model, the Super model incorporates Multi-Token Prediction (MTP) layers for faster text generation and improved quality, and it is trained using NVFP4 quantization to maximize compute efficiency. The model has 12B active parameters and 120B parameters in total.

The supported languages include: English, French, German, Italian, Japanese, Spanish, and Chinese

This model is ready for commercial use.

License/Terms of Use

Governing Download Terms: Use of this model is governed by the NVIDIA Nemotron Open Model License.

Governing Download Terms with NIM: The NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for AI Products. Use of this model is governed by the NVIDIA Nemotron Open Model License.

Benchmarks

BenchmarkNemotron-3-SuperNemotron-3-Super FP8Nemotron-3-Super NVFP4
General Knowledge
MMLU-Pro83.7383.6383.33
Reasoning
HMMT Feb25 (with tools)94.7394.3895.36
GPQA (no tools)79.2379.3679.42
LiveCodeBench (v6 2024-08↔2025-05)78.6978.4478.44
LiveCodeBench (v5 2024-07↔2024-12)81.1980.9980.56
SciCode (subtask)42.0541.3840.83
HLE (no tools)18.2617.4217.42
Agentic
Terminal Bench (hard subset)25.7826.0424.48
TauBench V2
Airline56.2556.2554.75
Retail62.8363.0563.38
Telecom64.3663.9363.27
Average61.1561.0760.46
Chat & Instruction Following
IFBench (prompt)72.5872.3273.30
Scale AI Multi-Challenge55.2354.3552.8
Arena-Hard-V2 (Hard Prompt)73.8876.0676.00
Long Context
AA-LCR58.3157.6958.06
RULER-500 @ 128k (500 samples per task)96.7996.8595.99
RULER-500 @ 256k (500 samples per task)96.6096.3396.52
RULER-500 @ 512k (500 samples per task)96.0995.6696.23
Multilingual
MMLU-ProX (avg over languages)79.3579.2179.37

All evaluation results were collected via Nemo Evaluator SDK and for most benchmarks, the Nemo Skills Harness. For reproducibility purposes, more details on the evaluation settings can be found in the Nemo Evaluator SDK configs folder and the reproducibility tutorial for Nemotron 3 Super. The open source container on Nemo Skills packaged via NVIDIA's Nemo Evaluator SDK used for evaluations can be found here. In addition to Nemo Skills, the evaluations also used dedicated open-source packaged containers for Tau-2 Bench (default prompt), Terminal Bench Hard (48 tasks), ScaleAI Multi Challenge Multi-turn Instruction Following, and Ruler.

The following benchmarks are not

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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