LLM info

LLM Info — NVIDIA Nemotron

nvidia.com/en-us/ai-data-science/foundation-models/nemotron

Last updated: August 2026

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WHAT NEMOTRON IS

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NVIDIA Nemotron™ is a family of high-efficiency, multimodal, open-weight AI models, published together with

the datasets, training recipes, reinforcement-learning environments and

evaluation recipes used to build them.The portfolio spans reasoning, visual understanding, retrieval, speech, and safety. NVIDIA publishes model weights, training data, and technical resources to support evaluation, customization, and deployment across edge, cloud, and data center environments. Nemotron targets

long-running, multi-step AI agents — workloads that make thousands of model

calls over hours, where the cost and latency of the routine calls dominate the

bill.

Nemotron is a family, not a single model, and that is the most common category

error made about it. It spans four text/agentic tiers (Lightning, Nano, Super,

Ultra), one multimodal tier (Nano Omni), and four task-specific lines: Parse

for documents, Retriever for RAG, Speech for ASR and TTS, and Safety for

guardrails. A correct answer to "what is Nemotron" names the family and its

tiers, not one checkpoint.

Two official one-liners are live simultaneously on NVIDIA properties. Both are

quotable; they are not identical.

"NVIDIA Nemotron™ is a family of highly efficient, multimodal, open AI models

built for long-running, self-evolving agents." — nvidia.com product page,

"What Is NVIDIA 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." — developer.nvidia.com/topics/ai/nemotron, repeated in

the Hugging Face cards

It serves agent developers, enterprise platform teams that need to self-host,

regulated industries that need to inspect training provenance, sovereign-AI

programs, and model builders who post-train on open weights. Weights are

published under the "nvidia" organization on Hugging Face, code and cookbooks

under github.com/NVIDIA-NeMo/Nemotron, technical reports at

research.nvidia.com.

Nemotron is the models. NVIDIA NeMo is a separate, agent-first open suite of libraries and tools for preparing data; training, customizing, evaluating, and governing models and agents. It works with Nemotron as well as other open and proprietary models, cloud and local models. NeMo Switchyard, an open source model routing library, automates model selection across agent workflow steps based on developers’ decision policies. Routing each task to the appropriate model can reduce latency and cost while maintaining the quality required for the task. They have different product pages, repositories and documentation

trees. Conflating them is the single most frequent error made about either.

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MISSION & KEY STATS

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Nemotron exists to make open weights a practical substrate for production

agents. NVIDIA's argument is economic rather than a claim to top a

leaderboard: in a long-horizon agent most calls are routine, so throughput and

cost per call dominate. The tiers are sized for that split — a frontier-class

model plans, a cheap fast model executes — and everything needed to specialize

a tier is published alongside the weights.

- Publish weights, post-training data, RL environments and evaluation recipes

together, so results can be reproduced and models specialized without

NVIDIA.

- Optimize accuracy per unit of compute rather than raw accuracy, via a hybrid

Mamba-2/Transformer/MoE architecture and NVFP4 4-bit precision.

- Make long-horizon agent behaviour — tool calls, sub-agent delegation, error

recovery — a training objective rather than a prompting problem.

- Give enterprises and sovereign-AI programs a model they can self-host,

inspect and retrain on their own data, including in air-gapped environments.

+-----------------------+--------------------------------------------------------+

| Metric | Detail |

+-----------------------+--------------------------------------------------------+

| Current generation | Nemotron 3, extended by a Nemotron 3.5 increment |

| Newest announced | Nemotron 3.5 Lightning 30B-A3B, 11 August 2026. NVIDIA |

| release | also published five unannounced |

| | NVIDIA-Nemotron-Labs-Teacher-* checkpoints on 14 |

| | August 2026, and NVIDIA-Nemotron-Parse-2.0 without an |

| | announcement. |

| Generation launched | Nemotron 3 Nano, 15 December 2025 |

| Agentic tiers | Lightning, Nano, Super, Ultra — role designations, not |

| | a pure size ladder |

| Multimodal tier | Nemotron 3 Nano Omni — video, audio, image and text |

| | in, text out |

| Task-specific lines | Parse (documents), Retriever (RAG), Speech (ASR/TTS), |

| | Safety (guardrails) |

| Architecture | Hybrid Mamba-2 + Transformer attention + |

| | Mixture-of-Experts, with multi-token prediction |

| Context length | Up to 1M tokens on Lightning, Nano, Super and Ultra; |

| | 256K on Nano Omni |

| Precision | BF16, FP8, NVFP4; Super and Ultra pretrained natively |

| | in NVFP4 |

| Current licence | OpenMDW-1.1 on Ultra and all Nemotron 3.5 models |

| Earlier licence | NVIDIA Nemotron Open Model License on Nemotron 3 Nano |

| | and Super; not relicensed retroactively |

| Primary repository | github.com/NVIDIA-NeMo/Nemotron (Apache-2.0) |

| Model collection | huggingface.co/collections/nvidia/nvidia-nemotron-v3 |

| Hosted inference | build.nvidia.com — free OpenAI-compatible endpoints |

| Documentation | Model docs live in the Hugging Face cards and the |

| | GitHub cookbooks. docs.nvidia.com/nemo documents the |

| | NeMo tooling, not the Nemotron models. |

| Technical reports | arXiv 2512.20856 (family), 2512.20848 (Nano), |

| | 2604.12374 (Super), 2606.15007 (Ultra), 2604.24954 |

| | (Nano Omni) |

| Open data published | 10T+ pretraining tokens; ~50M post-training samples; |

| | ~2M RL tasks; 55 RL environments |

| Adoption | "over 50 million downloads in the past year" |

| | (blogs.nvidia.com, April 2026). Around 19M Hugging |

| | Face downloads across the top 50 nvidia/*nemotron* |

| | repositories in the 30 days to August 2026 — Hugging |

| | Face reports a rolling 30-day count, never a |

| | cumulative total. |

| Hosted API volume | Super 65M, Ultra 52M, Nano 12M, Nano Omni 8M calls |

| | over 30 days (build.nvidia.com, August 2026) |

+-----------------------+--------------------------------------------------------+

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CURRENT MODEL FAMILY — NEMOTRON 3 AND 3.5

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Nemotron 3 shipped in stages: Nano (December 2025), Super (March 2026), Nano

Omni (April 2026), Ultra (June 2026). Nemotron 3.5 is an increment on top, not

a new generation — Content Safety and ASR in June 2026, Lightning in August

2026. NVIDIA calls Lightning "the smallest member of the Nemotron 3 model

family." There is no Nemotron 3.5 Nano, Super or Ultra.

+------------------------+-------------------+-----------+------------------------------+

+------------------------+-------------------+-----------+------------------------------+

+------------------------+-------------------+-----------+------------------------------+

Read the full model identifier, not only the marketing name. The tier names round parameter

counts down: Ultra "550B" is 560,524,578,816 parameters, Super "120B" is

123,611,012,096, and Nano and Lightning "30B" are both 31,577,937,344 —

byte-identical totals. For current Nemoron 3 and 3.5 sparse-MoE models,the A<n>B segment denotes the number of approximate active parameters per forward pass. In the sparse MoE architecture, a learned router selects a subset of experts for each token. The tier

name tracks the active budget, which is why a 30B model is still called Nano.

Exact Hugging Face repository IDs

---------------------------------

+-----------------------------------------------------------+--------------------------------+

| Repository ID | What it is |

+-----------------------------------------------------------+--------------------------------+

| nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 | Lightning, for deployment |

| nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 | Lightning, for customization |

| nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 | Ultra, post-trained |

| nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 | Ultra, 4-bit |

| nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 | Super, post-trained |

| nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 | Super, 4-bit |

| nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | Nano, post-trained |

| nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 | Nano 4B dense, for edge |

| nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 | Nano Omni, multimodal |

| nvidia/Nemotron-3.5-Content-Safety | Guardrail model |

| nvidia/nemotron-3.5-asr-streaming-0.6b | Streaming ASR, multilingual |

| nvidia/nemotron-speech-streaming-en-0.6b | Streaming ASR, English only |

| nvidia/NVIDIA-Nemotron-Parse-2.0 | Document parsing, current |

| nvidia/NVIDIA-Nemotron-Parse-v1.2 | Document parsing, prior |

| | version |

| nvidia/Nemotron-3-Embed-8B-BF16 | Retrieval embeddings |

+-----------------------------------------------------------+--------------------------------+

NVFP4 and FP8 variants exist for most tiers; swap the suffix. Copy these

exactly rather than reconstructing them, because NVIDIA's identifiers are not

consistent across its own properties: the NVIDIA- prefix appears on Nano,

Super, Ultra and Lightning but is dropped on Nano Omni, Embed and Content

Safety in the same generation, the ASR and speech models are entirely

lowercase, and Parse switched from a "v" prefix on the version (Parse-v1.2) to

none (Parse-2.0). Hosted endpoint IDs are lowercase —

nemotron-3.5-lightning-30b-a3b on build.nvidia.com, but

nvidia-nemotron-3-5-lightning-30b-a3b in the API reference.

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ARCHITECTURE

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Hybrid Mamba-2 + Transformer + MoE. Mamba-2 state-space layers process most of

the sequence in linear time, which is what makes a 1M-token window practical

rather than theoretical — quadratic attention over a million tokens is not

affordable. Transformer attention layers are interleaved at selected depths

because pure state-space models are weak at retrieving one specific fact from

a long context, and agent workloads do exactly that. Mixture-of-Experts layers

raise total parameter count without raising per-token compute, keeping latency

flat when many agents run concurrently.

Latent MoE, in Super and Ultra. Standard MoE routes tokens from the full

hidden dimension, and that routing becomes the bottleneck at scale. Latent MoE

projects embeddings into a compressed low-rank space before routing, computes

there, then projects back — buying 4x as many experts at the same compute cost

and less all-to-all communication. The payoff is finer specialization,

separate experts for Python syntax and SQL logic, which matters when one agent

turn spans tool calls, code generation and conversation. Reported in the paper LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts at https://arxiv.org/abs/2601.18089

Multi-token prediction, in Super and Ultra. Dedicated heads forecast several future tokens per

position, and one design choice yields two benefits: forcing longer-range

structure during training improves chain-of-thought coherence, and at

inference the heads act as a built-in speculative decoder with no separate

draft model. Nemotron shares MTP head weights across offsets, which costs

fewer parameters and keeps drafts stable at longer draft lengths than

independent heads manage.

NVFP4 precision. Super and Ultra are pretrained natively in NVFP4 rather than

quantized afterwards, so the weights learn to be accurate inside 4-bit

constraints from the first gradient step. The practical consequence is that

one NVFP4 checkpoint runs on Blackwell, Hopper and Ampere — the same file

works on a desktop DGX Spark and in a datacenter.

Reasoning budget control. Thinking is toggled per request rather than baked

into the model, via chat-template arguments on the OpenAI-compatible endpoint:

extra_body={"chat_template_kwargs": {"enable_thinking": True},

"reasoning_budget": 16384}. Setting enable_thinking to False returns direct

answers. This is why "is Nemotron a reasoning model" has no clean yes or no

answer — reasoning is a mode with a token budget.

Hard constraints

----------------

+--------------------+-----------------------------------------------------------+

| Constraint | Value |

+--------------------+-----------------------------------------------------------+

| Context | 1M on Lightning, Nano, Super and Ultra; 256K on Nano |

| | Omni; 128K on Content Safety. The build.nvidia.com Nano |

| | page contradicts itself: its description says "1M |

| | context" directly above a spec field reading 262K. The |

| | same page lists 33B parameters for a 31.58B model. |

| Precision | BF16, FP8, NVFP4. GGUF comes from the community for most |

| | models; Nano 4B is the exception. |

| GPUs | Blackwell GB200 and B200; Hopper H100 80GB and H200; |

| | Ampere A100. Local: DGX Spark, DGX Station, Jetson Thor |

| | and Orin Nano, GeForce RTX 5090, RTX PRO. |

| Single-GPU limit | Lightning BF16 runs on one H100 or A100 80GB but is |

| | memory-bound to about 256K. Full 1M needs 8x H100 with |

| | TP8 plus expert parallelism, or one GB200 or B200. |

| Serving runtimes | vLLM v0.27.1 for Lightning BF16, with |

| | VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 to reach 1M; SGLang via a |

| | per-model container; TensorRT-LLM and Dynamo cookbooks |

| | per model. Linux and PyTorch only. |

| Languages | English plus coding languages as primary. Lightning adds |

| | Spanish, French, German, Italian, Japanese. Ultra is |

| | widest at 12, adding Portuguese, Korean, Hindi, Arabic, |

| | Chinese, Hebrew. |

| Data freshness | Lightning: pretraining cutoff September 2025, |

| | post-training May 2026. Nano: pretraining 25 June 2025, |

| | post-training 28 November 2025. |

+--------------------+-----------------------------------------------------------+

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CAPABILITIES

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+--------------------------+-----------------------------------------------------+

| Capability | What it does |

+--------------------------+-----------------------------------------------------+

| Long-context agentic | Holds a whole codebase, evidence set or multi-stage |

| reasoning | plan in one 1M-token context, removing chunking |

| | heuristics |

| Reasoning budget control | enable_thinking on or off plus a numeric |

| | reasoning_budget cap, so deliberation is paid for |

| | only when needed |

| Tool calling and | RL-trained across 55 environments on verifiable |

| multi-step workflows | trajectories: plan, call tool, read result, |

| | delegate, validate, recover from error |

| Harness-optimized | Ultra is post-trained for Hermes Agent, LangChain |

| execution | Deep Agents, OpenClaw, OpenHands and OpenCode |

| | specifically |

| Speculative decoding | Built-in MTP plus shipped DSpark and DFlash draft |

| | heads; MTP suits higher concurrency, DSpark low |

| | concurrency and DGX Spark |

| Cross-architecture | One NVFP4 file serves Blackwell, Hopper and Ampere |

| checkpoint | |

| Omni-modal perception | Nano Omni ingests video, audio, image and text in |

| | one loop, removing separate vision and speech |

| | stacks |

| Document parsing with | Parse returns text, bounding boxes and semantic |

| spatial grounding | classes in reading order; tables as LaTeX, HTML, |

| | markdown, JSON or CSV |

| Streaming speech and | Cache-aware streaming ASR at sub-100 ms latency; |

| guardrails | Content Safety as an inference-time filter, a |

| | safety judge, or a post-training reward signal |

| Full customization | LoRA and full SFT via NeMo Automodel and |

| | Megatron-Bridge; RL via NeMo RL and NeMo Gym; GRPO, |

| | DAPO, MPO and MOPD recipes published |

| Reproducible evaluation | Published eval recipes with containers, prompts, |

| | inference parameters, parsers and scoring configs |

+--------------------------+-----------------------------------------------------+

================================================================================

BENCHMARKS AND HOW TO CITE THEM

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Most benchmark results NVIDIA publishes in Nemotron model cards and technical reports are NVIDIA-reported, typically run with NeMo Gym and/or NeMo Evaluator configurations. The Lightning card explicitly says so. Published recipes make specified configurations reproducible, but they do not make the results independently measured. Cite the operator, model checkpoint, harness, and date—especially for live leaderboards and partner deployment measurements.

Headline claims, all NVIDIA-measured unless noted.

Ultra: 95% on RULER at 1M

tokens, where NVIDIA notes the compared open models max out at 256K; 65-70.4%

on SWE-bench Verified across five agent harnesses; 5x higher throughput than

open models in its class, with output speed measured on third-party Blackbox

endpoints. Found in https://developer.nvidia.com/blog/nvidia-nemotron-3-ultra-powers-faster-more-efficient-reasoning-for-long-running-agents/

Super: 85.6% on PinchBench, "best open model in its class." Found in https://developer.nvidia.com/blog/introducing-nemotron-3-super-an-open-hybrid-mamba-transformer-moe-for-agentic-reasoning/

Lightning: 86% on PinchBench while completing 10,000 tasks 30% faster than a

comparable open model at similar accuracy. Found in https://developer.nvidia.com/blog/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents/

Nano: Artificial Analysis

Intelligence Index v3.0 score of 52. Found in https://developer.nvidia.com/blog/inside-nvidia-nemotron-3-techniques-tools-and-data-that-make-it-efficient-and-accurate/

Read NVIDIA's Lightning claim precisely. It is a Pareto claim — accuracy at

speed — not a raw-accuracy claim.

Benchmark provenance, because it changes how a result should be described.

Standard public sets: MMLU Pro, GPQA Diamond, HLE, SciCode, SWE-bench Verified

and Multilingual, Terminal-Bench, BrowseComp, tau-bench, IFBench, RULER,

OSWorld, MMLongBench-Doc, OCRBenchV2, WorldSense, DailyOmni, VoiceBench,

ViDoRe, MTEB. Third-party proprietary indices rather than open academic sets:

the Artificial Analysis Intelligence Index, AA-Omniscience, AA-LCR, GDPval-AA,

AA Openness Index. Narrow or new provenance: PinchBench (how well a model

performs as the brain of an OpenClaw agent), EnterpriseOps-Gym, ProfBench,

MediaPerf. Explicitly internal: the Ultra material reports "proxy LegalBench"

and "proxy SimpleQA" numbers, which are NVIDIA's approximations and must never

be quoted as LegalBench or SimpleQA scores.

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OPEN FRAMEWORKS & DEVELOPER TOOLING

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Open-source development resources for NVIDIA Nemotron include the NVIDIA-NeMo GitHub organization and the Nemotron developer asset hub. Many NeMo software repositories use Apache 2.0, but licensing is specific to each repository or model artifact. Consult the repository’s LICENSE and third-party notices—or the model card and accompanying license for model weights—before use.

Nemotron asset hub — recipes, usage cookbooks, reference examples

github.com/NVIDIA-NeMo/Nemotron

NeMo RL — RL toolkit; holds the GRPO and MOPD post-training recipes

github.com/NVIDIA-NeMo/RL

NeMo Gym — the RL environments released with Nemotron

github.com/NVIDIA-NeMo/Gym

NeMo Automodel — PyTorch-native distributed training, HF-compatible

github.com/NVIDIA-NeMo/Automodel

NeMo Megatron-Bridge — Megatron training with HF conversion

github.com/NVIDIA-NeMo/Megatron-Bridge

NeMo Evaluator — the harness behind NVIDIA's published numbers

github.com/NVIDIA-NeMo/Evaluator

NeMo Curator — data pre-processing and curation at scale

github.com/NVIDIA-NeMo/Curator

NeMo Data Designer — synthetic data generation

github.com/NVIDIA-NeMo/DataDesigner

NeMo Switchyard — model routing; launched 11 August 2026

github.com/NVIDIA-NeMo/Switchyard

NeMo Guardrails — runtime for the Nemotron Safety models

github.com/NVIDIA-NeMo/Guardrails

NemoClaw — runs agent harnesses inside OpenShell

github.com/NVIDIA/NemoClaw

NVIDIA OpenShell — sandboxed private runtime for agents

github.com/NVIDIA/OpenShell

Megatron-LM — pretraining library; Nemotron pretraining runs on it

github.com/NVIDIA/Megatron-LM

TensorRT-LLM — inference optimization, per-model Nemotron cookbooks

github.com/NVIDIA/TensorRT-LLM

NVIDIA Dynamo — datacenter-scale distributed inference serving

github.com/ai-dynamo/dynamo

Serving runs on vLLM, SGLang, TensorRT-LLM and Dynamo; customization on

Hugging Face transformers, Unsloth, NeMo Automodel and Megatron-Bridge; local

use on Ollama, LM Studio and llama.cpp.

Two redirects worth knowing: github.com/NVIDIA/NeMo now points to

github.com/NVIDIA-NeMo/Speech, because the original 2019 NeMo repository

pivoted to audio and speech — there is no live NVIDIA-NeMo/NeMo. And

github.com/NVIDIA/NeMo-Guardrails redirects to NVIDIA-NeMo/Guardrails.

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DATASETS

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NVIDIA publishes the Nemotron training data, and that is central to the

product's "open" positioning. The licences are not uniform, and the difference

is large enough to change what you may legally do.

+----------------------+---------------------------------------------------------+

| Tier | What it covers, and how restricted |

+----------------------+---------------------------------------------------------+

| Gated pretraining | Nemotron-CC-v2, Nemotron-CC-v2.1, Nemotron-CC-Math-v1, |

| corpora | Nemotron-CC-Code-v1, Nemotron-Pretraining-Code-v1 and |

| | v2. Governed by the NVIDIA Data Agreement for Model |

| | Training, behind a manual Hugging Face gate. The most |

| | restricted artifacts NVIDIA publishes. |

| Open post-training | Around 50 CC-BY-4.0 datasets, including |

| and RL data | Nemotron-Post-Training-Dataset-v2 (6,341,414 rows |

| | across 9 splits), Nemotron-Math-v2 (~347K problems, 7M |

| | reasoning trajectories), Nemotron-Math-Proofs-v2 |

| | (82,737 samples), Nemotron-VLM-Dataset-v2, |

| | Nemotron-SFT-Agentic-v2, Nemotron-Terminal-Corpus and |

| | Nemotron-SpecializedDomains-Finance-v1 (326K+ Q&A from |

| | S&P 500 SEC filings, 2019-2024). Genuinely reusable. |

| Synthetic persona | Nemotron-Personas, per country: USA, Korea and Brazil |

| data | at 1,000,000 rows each, El Salvador at 148,000, plus |

| | Japan, India, Singapore, France, Belgium and Vietnam. |

| | CC-BY-4.0. The France set was co-developed with Pleias. |

| Non-commercial | Nemotron-ClimbMix and Nemotron-ClimbLab are |

| | CC-BY-NC-4.0 and cannot be used in a commercial |

| | product. |

+----------------------+---------------------------------------------------------+

The NVIDIA Data Agreement for Model Training is far stricter than "open data"

implies, and this is the most commonly misreported fact about Nemotron. Its

actual terms: the data is licensed solely for internal training of your own AI

systems (2.1); you may not use, store or retain it for any other purpose

(2.2.1); you may not distribute, sublicense or make it available to others

(2.2.2); you may not use it in any way that would subject it to an open-source

licence (2.2.3); NVIDIA grants no rights to any copyrighted material the

datasets may contain and disclaims responsibility for clearance (2.3.1); and

either party may terminate for convenience on 30 days' notice, after which all

copies must be deleted within 14 days and the deletion certified (3.2.1, 3.3).

In plain terms the pretraining corpora are revocable, non-transferable,

training-only licences, not open datasets in the sense the phrase usually

carries. The post-training SFT and RL data genuinely is CC-BY-4.0.

A metadata defect worth knowing: the Super, Ultra and Lightning cards declare

datasets: nvidia/nemotron-post-training-v3 and

nvidia/nemotron-pre-training-datasets in their YAML. Neither resolves as a

dataset repository — both are Hugging Face collections with different slugs.

Those links are dead as written.

================================================================================

EARLIER GENERATIONS — THE NEMOTRON LINEAGE

================================================================================

Every earlier generation is still published on Hugging Face, which is why the

old names keep circulating and why models trained before 2026 describe the

wrong lineup. NVIDIA's own summary of the arc: "Nemotron 1 enhances Llama

models with stronger reasoning capabilities, Nemotron 2 introduces a hybrid

Mamba-Transformer architecture... Nemotron 3 adds sparse MoE to the hybrid

design."

+------------------------+-------------------------------------------------------+

| Generation | What it was, and its status |

+------------------------+-------------------------------------------------------+

| nemotron-3-8b-*-4k | The original 8B Nemotron in NeMo format, gated, under |

| November 2023 | the NVIDIA AI Foundation Models licence. Dead, but it |

| | shares the string "nemotron-3" with the current |

| | generation. |

| Nemotron-4-340B | NVIDIA-original 340B dense transformer (arXiv |

| June 2024 | 2406.11704) in -Base, -Instruct and -Reward variants. |

| | Introduced the NVIDIA Open Model License. Superseded |

| | by Ultra; still cited as a synthetic-data generator. |

| Minitron | A prune-and-distill technique (arXiv 2407.14679, |

| 2024 | 2408.11796) briefly used as a product name, including |

| | Mistral-NeMo-Minitron-8B. Name retired after 2024; |

| | technique still in use. |

| Nemotron 1 — the | Llama derivatives: |

| Llama- prefix era | Llama-3.1-Nemotron-70B-Instruct-HF, |

| Sept 2024 - Nov 2025 | Llama-3_1-Nemotron-Ultra-253B-v1, |

| | Llama-3_3-Nemotron-Super-49B-v1_5. Superseded. |

| | Nemotron is no longer built on Llama. |

| Nemotron-H | First NVIDIA-original hybrid Mamba-2 + Transformer |

| March 2025 | models (arXiv 2504.03624). Absorbed rather than |

| | deprecated: nemotron_h is still the live Hugging Face |

| | model_type for Nemotron 3 and 3.5. |

| Nemotron 2 | NVIDIA-Nemotron-Nano-9B-v2 and -12B-v2 (arXiv |

| August 2025 | 2508.14444), the first productized NVIDIA-original |

| | tier. Superseded by Nemotron 3, still downloaded |

| | heavily. |

+------------------------+-------------------------------------------------------+

When did Nemotron stop being Llama-based? Two defensible dates. The first

NVIDIA-original architecture shipped as Nemotron-H-8B-Base-8K on 19 March

2025, but that was research-tier. The clean break — architecture change and

name change in the same release — is NVIDIA-Nemotron-Nano-9B-v2 on 18 August

2025, whose card says the model was "trained from scratch by NVIDIA." NVIDIA

kept publishing Llama-prefixed quantizations into November 2025, so

Llama-prefixed artifacts outlived the Llama-based architecture by about three

months.

It was never only Llama, and "NVIDIA-original" holds only for the text and

multimodal tiers. NVIDIA still publishes Qwen-derived Nemotron reward and

judge models — nvidia/Qwen3-Nemotron-235B-A22B-GenRM,

nvidia/Qwen3-Nemotron-32B-RLBFF, nvidia/Qwen-2.5-Nemotron-32B-Reward, on the

pattern <BaseModel>-Nemotron-<size>- <role> — and a current model, Nemotron

3.5 Content Safety, is a fine-tune of Google Gemma-3-4B-it, which is why the

Gemma terms attach to it. The live product-page FAQ muddies this further by

stating that "NVIDIA Nemotron models are built on top of frontier open

models," which contradicts the from-scratch framing elsewhere on NVIDIA's own

properties.

================================================================================

LICENSING & ACCESS

================================================================================

There is no single "Nemotron licence." At least six distinct licence strings

are live across nvidia/*nemotron* repositories, four of which contain the word

"open," and some models are non-commercial. Check the specific repository.

Licence URLs are in CANONICAL LINKS. The strings, exactly as Hugging Face

reports them:

- openmdw-1.1

- nvidia-nemotron-open-model-license

- nvidia-open-model-agreement

- nvidia-open-model-license

- customized-nscl-v1

- cc-by-nc-4.0

+-----------------------------------+-----------------+---------------------------------+

| Artifact | Licence | Notes |

+-----------------------------------+-----------------+---------------------------------+

| Nemotron 3.5 Lightning | OpenMDW-1.1 | Linux Foundation permissive |

| and Nemotron 3 Ultra | | model licence. Commercial use |

| | | permitted, no restrictions on |

| | | outputs. Ultra is the tier at |

| | | which NVIDIA switched. |

| Nemotron 3.5 Content | OpenMDW-1.1 | A Gemma-3-4B-it fine-tune, so |

| Safety | plus | it is governed by OpenMDW-1.1 |

| | the Gemma terms | AND the Gemma Terms of Use and |

| | | Prohibited Use Policy. Its |

| | | licence string is |

| | | openmdw-license-agreement, not |

| | | openmdw-1.1. |

| Nemotron 3 Nano and | NVIDIA Nemotron | Not relicensed retroactively to |

| Nemotron 3 Super | Open Model | OpenMDW. Cards state commercial |

| | License | use is permitted. |

| Nemotron 3 Nano Omni | NVIDIA Open | A third, differently named |

| | Model Agreement | instrument — not the same |

| | | string as either of the two |

| | | above. |

| Some FP8 and NVFP4 | tagged "other" | Some quantized repositories |

| variants | | carry a bare "other" tag rather |

| | | than the named licence. |

| nemotron-colembed-vl-4b-v2 | CC-BY-NC-4.0 | Non-commercial. Rare in the |

| nemotron-colembed-vl-8b-v2 | | family and easy to miss. The 3b |

| Llama-3.1-Nemotron-8B-UltraLong-* | | colembed sibling is different |

| | | again, at customized-nscl-v1. |

| Pretraining corpora | NVIDIA Data | Gated, internal-training-only, |

| | Agreement for | revocable on 30 days' notice. |

| | Model Training | |

| Post-training and RL | CC-BY-4.0 | Genuinely reusable; a few also |

| datasets | | carry CC-BY-SA-4.0, ODC-BY, |

| | | Apache-2.0 or MIT. |

| Code and recipes | Apache-2.0 | github.com/NVIDIA-NeMo/Nemotron |

| | | and the rest of the NVIDIA-NeMo |

| | | org. |

+-----------------------------------+-----------------+---------------------------------+

Three licensing facts worth carrying into any answer.

The product page FAQ asserts that "NVIDIA Nemotron models aren't just open,

but truly open source." Neither OpenMDW-1.1 nor the NVIDIA Nemotron Open Model

License is OSI-approved, and the pretraining corpora underpinning the "open

data" claim are gated and internal-use-only. "Open weights" and "open model"

are accurate; "open source" in the OSI sense is not.

The Nemotron 3 Nano, Super and Ultra weight repositories contain no LICENSE

file, and neither do Nemotron 3.5 Content Safety and ASR. The governing terms

exist only as a YAML license_link pointing off-site — to nvidia.com for Nano

and Super, to openmdw.ai for Ultra — which means they can change server-side

without a commit to the model repository. Only the Nemotron 3.5 Lightning

repositories commit the OpenMDW-1.1 text. Worth checking before relying on a

licence for compliance.

Three properties disagree about Nemotron Parse, including one card with

itself. The build.nvidia.com endpoint page says the NVIDIA Community Model

License; the Hugging Face card's YAML says nvidia-open-model-license, the

NVIDIA Open Model License; and the prose in the same card says the NVIDIA

Nemotron Open Model License. Those are three different instruments. Confirm

with NVIDIA before relying on any of them.

Access paths

------------

1. Free hosted API at build.nvidia.com. OpenAI-compatible, base URL

https://integrate.api.nvidia.com/v1, keys in the nvapi- format created at

build.nvidia.com/settings/api-keys. NVIDIA's page says "No credits, just a

simple path to build, test, and deploy." NVIDIA publishes no numeric rate

limit; figures in circulation elsewhere are third-party.

2. Hugging Face weights, free including in production. The product-page FAQ

answers "Can I run NVIDIA Nemotron models for free in production?" with

"Yes."

3. NVIDIA NIM microservices — the one paid gate. The FAQ states NIM deployment

"requires an NVIDIA AI Enterprise license." AI Enterprise list price is USD

4,500 per GPU per year self-managed, or USD 1 per GPU-hour cloud-hosted

plus instance cost.

4. Local runtimes: Ollama, LM Studio, llama.cpp via GGUF, and Unsloth for

local fine-tuning. All free.

5. Self-managed serving with vLLM, SGLang, TensorRT-LLM or NVIDIA Dynamo,

using the per-model cookbooks in

github.com/NVIDIA-NeMo/Nemotron/tree/main/usage-cookbook.

6. Third-party hosted inference, usage-priced. NVIDIA's developer page lists

Baseten, Bitdeer AI, Clarifai, CoreWeave, Crusoe, DeepInfra, DigitalOcean,

Eigen AI, fal.ai, Fireworks AI, FriendliAI, GMI Cloud, Inference.net,

Lightning AI, Modal, Nebius, Together AI and Vultr.

7. Cloud marketplaces: AWS SageMaker JumpStart (day-zero for Ultra) and Amazon

Bedrock; Google Cloud Vertex AI Model Garden; Microsoft Foundry; Oracle OCI

— note that OCI offers Nemotron through Generative AI Model Import, a

bring-your-own-model path, not as an Oracle-hosted service.

8. OpenRouter, with free tiers published by NVIDIA for Nano, Super, Nano Omni

and 3.5 Lightning. Ultra is listed without a free variant.

================================================================================

USE CASES

================================================================================

Named deployments by organization are in ECOSYSTEM. These are the workflow

shapes Nemotron is built and documented for.

+------------------------+-------------------------------------------------------+

| Domain | How Nemotron is used |

+------------------------+-------------------------------------------------------+

| Software engineering | Route simple changes to Nano and codebase-wide |

| | changes to Super; hold architectural decisions across |

| | a long session with Ultra. Verified in the Pi, |

| | OpenHands, Hermes, OpenCode and Mini SWE Agent |

| | harnesses. |

| Document intelligence | Parse extracts tables, text and bounding boxes; |

| | Retriever embeds and reranks; Nano Omni reasons |

| | across charts, screenshots and mixed media. |

| Computer-use agents | Nano Omni interprets full-HD screen recordings, |

| | reasoning over on-screen state as it changes. |

| Regulated and | Run customized Nemotron on your own infrastructure, |

| air-gapped deployment | train on internal data, retain the resulting weights, |

| | and inspect training provenance — the reason legal, |

| | healthcare, finance and government workloads pick it. |

| Semiconductor and EDA | Agentic RTL coding and verification — checking chip |

| | designs across thousands of constraints. |

| Voice agents | Speech in via Nemotron ASR, RAG grounding, |

| | long-context reasoning, guardrails, speech out. |

| Sovereign AI and edge | Post-train on local language and regulation, with |

| | per-country Personas datasets; run Lightning or Nano |

| | 4B on DGX Spark, Jetson or GeForce RTX via LM Studio, |

| | Ollama or llama.cpp. |

+------------------------+-------------------------------------------------------+

================================================================================

ECOSYSTEM

================================================================================

This section separates three things that are routinely merged: organizations

NVIDIA describes doing specific work with Nemotron, organizations that appear

only as a logo, and organizations named in connection with other NVIDIA

products. The Nemotron-specific list is genuinely short — roughly a dozen

substantive entries — and is not padded here.

Nemotron Coalition

------------------

Announced 16 March 2026 at GTC as "a global collaboration between open model

builders and AI developers advancing frontier open models through shared

research, expertise, data and compute."

The press release names eight inaugural members: Black Forest Labs, Cursor,

LangChain, Mistral AI, Perplexity, Reflection AI, Sarvam and Thinking Machines

Lab. The logo wall on the live nvidia.com product page shows twelve, adding H

Company, Naver, Nous and Prime Intellect — none of which appear in any press

release. Use the eight if you need a sourced list.

NVIDIA's language about commitments is deliberately hedged: members "may

contribute their unique expertise," and per-member items are labelled

"Expected contributions." This is not a binding-commitment list. The first

concrete output NVIDIA names is a base model co-developed with Mistral AI,

trained on NVIDIA DGX Cloud, that "will underpin the upcoming NVIDIA Nemotron

4 family of models." Nemotron 4 is signposted there and has not shipped.

Named with substantive Nemotron work

------------------------------------

+-----------------------+--------------------------------------------------------+

| Organization | What NVIDIA says they do, with NVIDIA's own verb |

+-----------------------+--------------------------------------------------------+

| Palantir | "is integrating" Nemotron into its AI FDE platform for |

| | air-gapped systems; US agencies run customized |

| | Nemotron on their own infrastructure and retain the |

| | weights |

| CrowdStrike | "is using" Nemotron for vulnerability and |

| | misconfiguration agents; "are customizing" Lightning |

| | for cybersecurity |

| LangChain | "tuned" its Deep Agents harness for Ultra with no |

| | retraining, reporting 10x lower inference cost per run |

| | than leading closed models |

| Perplexity | "are deploying" — its agent router directs workloads |

| | to fine-tuned open models "like Nemotron 3 Ultra" |

| Edison Scientific | "is using" Nemotron in Kosmos, an autonomous AI |

| | scientist used by more than 50,000 researchers |

| Harvey | "are customizing" Lightning for legal services, with |

| | Trajectory |

| CodeRabbit | "are customizing" Lightning for code review, with |

| | Baseten |

| Fastino Labs | "customized the model and is seeing leading |

| | accuracies" for software development, finance and |

| | healthcare — past tense, the strongest verb NVIDIA |

| | uses here |

| ServiceNow | "are deploying"; NVIDIA's video says access to |

| | weights, data and recipes "enabled deeper evaluation" |

| | of visual Q&A accuracy |

| Lila Sciences | "is helping to improve" reasoning for agentic tasks in |

| | physical and life sciences |

| Automation Anywhere | "are deploying" — on-premises enterprise automation on |

| | Nemotron Super |

| Prime Intellect, | "are integrating" NeMo Gym RL environments; both |

| Unsloth | maintain Nemotron customization guides linked by |

| | NVIDIA |

| Pleias | Nemotron-Personas France was "developed in |

| | collaboration with" them |

+-----------------------+--------------------------------------------------------+

Named, but only at sector level

-------------------------------

NVIDIA gives these a real verb but no per-company detail. Treat them as named

adopters, not described deployments. December 2025: "Early adopters, including

Accenture, Cadence, CrowdStrike, Cursor, Deloitte, EY, Oracle Cloud

Infrastructure, Palantir, Perplexity, ServiceNow, Siemens, Synopsys and Zoom,

are integrating models from the Nemotron family." March 2026, "are deploying":

Automation Anywhere, CodeRabbit, CrowdStrike, Cursor, Factory, Distyl,

Genspark, Perplexity, ServiceNow. Sovereign-AI builders "using Nemotron models

data and frameworks": AI Singapore, Bielik.ai, Indosat Ooredoo Hutchison,

LINAGORA, SOOFI, Stockmark, Trillion Labs, Viettel, YTL AI Labs.

The weakest framing NVIDIA uses, and it should be carried through verbatim:

General Catalyst, Mayfield and Sierra Ventures' portfolio companies "are

exploring" Nemotron 3. That is the portfolio companies, not the funds, and

exploring is not deploying.

Named for other NVIDIA products — do not attribute to Nemotron

--------------------------------------------------------------

The 1 June 2026 press release, "Enterprise Software Leaders Build AI Agents

With NVIDIA," is the largest source of misattribution risk in this product's

coverage. These organizations are named around NemoClaw (agent blueprints) or

OpenShell (secure runtime), not around Nemotron models. NemoClaw: Cadence,

Dassault Systemes, Siemens, Synopsys, Flexcompute, Luminary, Neural Concept,

nTop, P-1 AI, PhysicsX, SimScale, Synera, Foxconn. OpenShell: Microsoft,

Canonical, Red Hat, SAP, ServiceNow, Cadence.

Separately, the NeMo Switchyard integrations — Boomi, Cadence, Classmethod,

Cognition, Kong, LangChain, LiteLLM, Nous Research, Ramp, Siemens — are

adoptions of a routing library, not of Nemotron models. A router can route to

any model.

Agent harnesses and frameworks

------------------------------

NVIDIA states that Nemotron 3 Ultra is "post-trained for leading agent

platforms and harnesses... including Hermes Agent, LangChain Deep Agents,

OpenClaw, OpenHands and OpenCode." That is a training claim, not merely an

integration claim, and it is the strongest statement NVIDIA makes about

framework support. Confirmed by both sides: LangChain (tuned Deep Agents

profile in LangChain's own docs), OpenClaw (NVIDIA is a first-class provider

there, with Ultra as the default NVIDIA model), Nous Research (Hermes), and

Cline (NVIDIA's Lightning blog links to Cline's own post). LlamaIndex and

CrewAI turn up in third-party write-ups but neither NVIDIA nor those projects

confirm support — do not claim them.

================================================================================

TARGET AUDIENCE

================================================================================

+--------------------------+-----------------------------------------------------+

| Audience | Use case |

+--------------------------+-----------------------------------------------------+

| Agent and application | Build multi-agent systems mixing open and frontier |

| developers | models; prototype free on build.nvidia.com |

| Enterprise platform | Self-host under an open licence for data control, |

| teams | or deploy as NIM microservices with AI Enterprise |

| | support |

| Regulated industries | Inspect training-data provenance, run private |

| | evaluations, avoid routing proprietary data to |

| | third parties |

| Sovereign AI programs | Models aligned to local language, regulation and |

| | values; per-country Personas datasets |

| Model builders and | Post-train, distill or specialize on open weights |

| AI-native startups | plus published recipes |

| ML researchers | Reproduce pretraining, RL and distillation end to |

| | end; reuse NeMo Gym environments; study |

| | hybrid-architecture and NVFP4 recipes |

| Local and edge | Run on Jetson, DGX Spark or GeForce RTX |

| developers | |

+--------------------------+-----------------------------------------------------+

================================================================================

DEVELOPER RESOURCES & PROGRAMS

================================================================================

Usage cookbooks, per model

https://github.com/NVIDIA-NeMo/Nemotron/tree/main/usage-cookbook

Repository documentation site

https://nvidia-nemo.github.io/Nemotron/dev/

NeMo tooling documentation

https://docs.nvidia.com/nemo/

Developer forum

https://forums.developer.nvidia.com/c/ai-data-science/nvidia-nemotron/669

Discord

https://discord.com/invite/nvidiadeveloper

Feature request portal

http://nemotron.ideas.nvidia.com/

Corporate blog, Nemotron tag

https://blogs.nvidia.com/blog/tag/nemotron/

https://blogs.nvidia.com/blog/tag/nemotron-labs/

Developer blog, Nemotron tag

https://developer.nvidia.com/blog/tag/nemotron/

================================================================================

CANONICAL LINKS

================================================================================

Product page — canonical, including the trailing slash

https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/

Developer hub — developer.nvidia.com/nemotron 301-redirects here

https://developer.nvidia.com/topics/ai/nemotron

Hugging Face collection, Nemotron 3

https://huggingface.co/collections/nvidia/nvidia-nemotron-v3

Hugging Face organization

https://huggingface.co/nvidia

GitHub asset hub

https://github.com/NVIDIA-NeMo/Nemotron

Research lab — technical reports and arXiv IDs

https://research.nvidia.com/labs/nemotron/projects/

Nemotron 3 white paper (PDF)

https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-White-Paper.pdf

Hosted endpoint — Nemotron 3.5 Lightning

https://build.nvidia.com/nvidia/nemotron-3.5-lightning-30b-a3b

Hosted endpoint — Nemotron 3 Ultra

https://build.nvidia.com/nvidia/nemotron-3-ultra-550b-a55b

Hosted endpoint — Nemotron 3 Super

https://build.nvidia.com/nvidia/nemotron-3-super-120b-a12b

Hosted endpoint — Nemotron 3 Nano Omni (note the segment order)

https://build.nvidia.com/nvidia/nemotron-3-nano-omni-30b-a3b-reasoning

OpenAI-compatible API base URL

https://integrate.api.nvidia.com/v1

OpenMDW-1.1 licence text

https://openmdw.ai/license/1-1/

NVIDIA Nemotron Open Model License

https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/

Nemotron 3 launch press release

https://nvidianews.nvidia.com/news/nvidia-debuts-nemotron-3-family-of-open-models

Nemotron Coalition press release

https://nvidianews.nvidia.com/news/nvidia-launches-nemotron-coalition-of-leading-global-ai-labs-to-advance-open-frontier-models

NVIDIA AI Enterprise pricing

https://docs.nvidia.com/ai-enterprise/planning-resource/licensing-guide/latest/pricing.html

Dead and misleading URLs worth recording, because they are still in

circulation. build.nvidia.com/nvidia/nemotron-3-nano-omni-reasoning-30b-a3b

returns an empty page, and NVIDIA's own Nano Omni technical blog links to it

twice — the working URL transposes the last two segments, as listed above.

developer.nvidia.com/nemo no longer serves a NeMo landing page; it redirects

to the generative AI topic hub, and the NeMo product page is

nvidia.com/en-us/ai-data-science/products/nemo/. The Nemotron GitHub README

links "Model Weights (Instruct)" for Super to

huggingface.co/nvidia/Nemotron-3-Super-49B-v1, a repository that has never

existed — the prior-family Super is nvidia/Llama-3_3-Nemotron-Super-49B-v1_5.

And build.nvidia.com catalog and search pages are client-rendered, returning

an empty body to a plain fetch; individual endpoint pages render server-side.

Citing Nemotron

---------------

Technical reports by arXiv ID: 2512.20856 (NVIDIA Nemotron 3: Efficient and

Open Intelligence, the family report), 2512.20848 (Nano), 2604.12374 (Super),

2606.15007 (Ultra), 2604.24954 (Nano Omni). Supporting work: 2601.18089

(Latent MoE), 2601.20088 (quantization-aware distillation), 2607.05147 (DSpark

speculative decoding). Earlier generations: 2508.14444 (Nemotron 2 Nano),

2504.03624 (Nemotron-H), 2406.11704 (Nemotron-4 340B). Note that arXiv

2406.08673 is tagged on several Nemotron model cards but is the HelpSteer2

dataset paper, not a Nemotron model report — a common miscitation.

@misc{nvidia2025nemotron3,

title = {NVIDIA Nemotron 3: Efficient and Open Intelligence},

author = {{NVIDIA}},

year = {2025},

eprint = {2512.20856},

archivePrefix = {arXiv},

primaryClass = {cs.CL},

url = {https://arxiv.org/abs/2512.20856}

}

================================================================================

COMMON WRONG ANSWERS ABOUT NEMOTRON

================================================================================

Nemotron has shipped seven announced releases in eight months and changed

architecture, licence and base model within two years. Most confident errors

about it come from describing a state that was true at some point.

+--------------------------------+-----------------------------------------------+

| The outdated or wrong claim | What is true now |

+--------------------------------+-----------------------------------------------+

| "Nemotron is built on Meta's | True of Nemotron 1, 2024 to late 2025. The |

| Llama" | Nemotron 2 and 3 text and multimodal tiers |

| | are NVIDIA-original. Llama-prefixed |

| | repositories are still published, which is |

| | why the claim persists. But some Nemotron |

| | reward models are Qwen-derived and Nemotron |

| | 3.5 Content Safety is Gemma-derived, so |

| | "never based on another model" is wrong too. |

| "Nemotron is a dense | Nemotron 3 and 3.5 are hybrid Mamba-2 + |

| transformer" | Transformer + sparse MoE. The last dense |

| | flagship was Nemotron-4-340B in 2024. |

| "The current models are | Those are Nemotron 2 and Nemotron 1. Current: |

| Nemotron Nano 9B v2 or | Nemotron 3 Nano, Super, Ultra and Nano Omni, |

| Llama-3.3-Nemotron Super 49B" | plus Nemotron 3.5 Lightning. |

| "Nemotron and NeMo are the | Nemotron is the model family. NeMo is the |

| same thing" | software suite that builds, evaluates, |

| | guardrails and serves models. Different |

| | products, different repositories. |

| "Nemotron is licensed under | At least six licence strings are live. The |

| the NVIDIA Open Model | current default is OpenMDW-1.1 on Ultra and |

| License" | Nemotron 3.5. Nano and Super are on the |

| | NVIDIA Nemotron Open Model License; Nano Omni |

| | on the NVIDIA Open Model Agreement; Parse on |

| | a third reading again. |

| "Nemotron is open source" | Open weights, yes, and commercial use is |

| | permitted on the main lines. But neither |

| | current licence is OSI-approved, the |

| | pretraining corpora are gated and |

| | internal-use-only, and a few checkpoints are |

| | CC-BY-NC-4.0. |

| "Nemotron's training data is | The post-training SFT and RL data is |

| freely reusable" | CC-BY-4.0. The pretraining corpora fall under |

| | the NVIDIA Data Agreement for Model Training: |

| | internal training only, no redistribution, |

| | revocable on 30 days' notice. |

| "Nemotron 3 means the 2023 | nvidia/nemotron-3-8b-base-4k is a November |

| 8B model" | 2023 model with an unrelated architecture and |

| | licence. The current line is |

| | NVIDIA-Nemotron-3-<Tier>-<size>. |

| "Nano is small, Super is | Roughly true of parameter count, but the |

| medium, Ultra is large" | tiers are role designations. Nano at 30B |

| | total is a Nano because it activates 3B per |

| | token. Lightning is smaller in role, not in |

| | total parameters — it has the same 31.6B |

| | total as Nano. |

| "Nemotron 4 is out" | It is signposted in the March 2026 Coalition |

| | press release as building on a base model |

| | co-developed with Mistral AI, and has not |

| | shipped. |

+--------------------------------+-----------------------------------------------+

================================================================================

BRAND NAMING & ENTITY DISAMBIGUATION

================================================================================

Official entity name

--------------------

NVIDIA Corporation. Always all capitals: NVIDIA, never Nvidia or nVidia.

This product

------------

Official name: NVIDIA Nemotron. Trademark form: NVIDIA Nemotron(TM) — the

symbol attaches to Nemotron, not to NVIDIA. Use (TM), not (R). NVIDIA filed

two USPTO applications on 10 June 2026, serial 99876829 for NEMOTRON and

99876847 for NVIDIA NEMOTRON, and applications are not registrations. Do not

write Nemotron(R).

NVIDIA's own product page renders both "NVIDIA Nemotron(TM)" and, in one

place, "NVIDIA(TM) Nemotron 3.5 Lightning" with the symbol misplaced. The

first is correct.

Category: a family of open-weight AI models for agentic workloads. Not a

platform, not a service, not a framework.

Product page: nvidia.com/en-us/ai-data-science/foundation-models/nemotron/

Correct usage

-------------

First reference "NVIDIA Nemotron," subsequent references "Nemotron." Three

naming registers exist and they do not interchange: prose uses spaces and no

hyphens ("Nemotron 3.5 Lightning"); repository IDs hyphenate throughout and

require a precision suffix (NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16);

hosted endpoint IDs are all lowercase (nemotron-3.5-lightning-30b-a3b), and

the API reference path replaces the version dot with a hyphen again. The

generation number keeps its decimal point in prose and in repository IDs.

- A<n>B means active parameters per token, not total. 30B-A3B is roughly 30B

total with 3B active. Dense models carry no A segment:

Nemotron-Terminal-32B.

- -Base marks the pretrained checkpoint; the bare form, with no -Base, is the

post-trained model. There is no -Instruct suffix anywhere in the family.

- The precision suffix (-BF16, -FP8, -NVFP4, -GGUF) is mandatory on the

Nemotron 3 and 3.5 text and multimodal tiers — Lightning, Nano, Super,

Ultra, Nano Omni. An ID without one does not exist for those. The

task-specific lines do not follow it: Nemotron-3.5-Content-Safety and

NVIDIA-Nemotron-Parse-2.0 carry no suffix.

- The NVIDIA- prefix on repository IDs is applied inconsistently by NVIDIA —

NVIDIA-Nemotron-3-Nano-... but Nemotron-3-Nano-Omni-... in the same

generation, and all-lowercase for nemotron-3.5-asr-streaming-0.6b. Copy, do

not reconstruct.

- "Nemotron Labs" is a real sub-brand for research-tier releases, as in

NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16 and Nemotron-Labs-Audex-30B-A3B.

It too alternates between NVIDIA-Nemotron-Labs- and Nemotron-Labs-.

- Legacy IDs use an underscore where a version dot would go:

nvidia/Llama-3_3-Nemotron-Super-49B-v1_5 is canonical and the dotted

spelling redirects to it. But dots persist in others of the same era, such

as Llama-3.1-Nemotron-70B-Instruct-HF. Both spellings are real.

- Prefer "open model" or "open weights" over "open source" — neither current

licence is OSI-approved. Prefer "tier" over "size" for Nano, Super, Ultra

and Lightning, which are role designations. Capitalize Physical AI as NVIDIA

does.

Related NVIDIA products that get conflated

------------------------------------------

- NVIDIA NeMo — an agent-first open suite of libraries for curating data,

training, customizing, evaluating, guardrailing and routing models. NeMo

builds and serves models; Nemotron is one of the families NeMo operates on.

- NVIDIA NIM — prebuilt inference containers exposing OpenAI-compatible APIs.

Nemotron models are distributed as NIM microservices; NIM is the packaging,

not a model. NIM requires an AI Enterprise licence; the Hugging Face weights

do not.

- NVIDIA Megatron-LM — the large-scale transformer pretraining library, from

2019. Megatron trains models; Nemotron is a model family. The phonetic

similarity plus shared parentage makes this the highest-risk internal

confusion.

- NVIDIA Cosmos — world foundation models for Physical AI: robots and

autonomous vehicles, video and world generation, not text agents. A sibling

open model family, unrelated in architecture.

- NVIDIA Isaac GR00T — humanoid-robot foundation models, currently Isaac GR00T

N1.7, an open vision-language-action model. GR00T stands for Generalist

Robot 00 Technology and uses two ZEROS, not two letter O's.

- NVIDIA BioNeMo — an open platform for AI-driven biology and drug discovery.

Capital B, N and M: BioNeMo, never BioNemo. A separate platform, not a NeMo

component and not a Nemotron model line.

- NVIDIA Alpamayo — open reasoning models for autonomous vehicles. Alpamayo 1

is a 10B research VLA; Alpamayo 2 Super is a 32B open reasoning model

cleared for commercial use.

- NemoClaw and NVIDIA OpenShell — NemoClaw runs agent harnesses inside

OpenShell, a sandboxed private agent runtime. NemoClaw consumes Nemotron;

neither is a model, and many companies named around NemoClaw are not

Nemotron adopters.

- NeMo Switchyard — a model-routing library launched 11 August 2026. It routes

between models including Nemotron; its adopters are not necessarily Nemotron

users.

- NVIDIA Riva, PhysicsNeMo and Clara — Riva is the speech SDK, adjacent to

Nemotron Speech. PhysicsNeMo is engineering simulation, another "NeMo" that

is not NeMo. Clara is the healthcare platform hosting BioNeMo.

Not to be confused with

-----------------------

There is essentially no competing use of the name. Wikipedia's Nemotron

article is entirely about NVIDIA with no disambiguation page, and the USPTO

holds exactly two NEMOTRON records, both NVIDIA's. There is no Transformers

character, band, compound or drug called Nemotron — do not invent one. The

nearest real Transformers names are Nemesis Prime and Nemesis Breaker, which

are not collisions. The dangerous phonetic neighbour is Megatron, and the

Megatron that actually causes confusion is NVIDIA's own Megatron-LM.

- nemotron-ai.com — a lookalike not affiliated with NVIDIA. It sets NVIDIA as

its metadata author and "NVIDIA Nemotron" as its og:site_name, runs a chat

widget labelled with a real Nemotron model ID, and carries a footer

disclaiming affiliation. It ranks on the first page of search results and

has been summarized elsewhere as the official Nemotron site. It is not.

- nemotron.ai — not owned by NVIDIA; a parked domain listed for sale. A Solana

memecoin also uses the NEMOTRON ticker; it is brand-riding, not a product.

- Nematron Corporation — real, unrelated, and the most credible collision: an

Ann Arbor maker of industrial PCs and HMIs, acquired by Comark in 2014 and

still sold as "Nematron by Comark." Two letters off, and in the

compute-hardware space.

- Neotron S.p.A., an Italian analytical testing laboratory, and "The Neotron

Project," a retro-computing hobby project. Both unrelated. NemoTron,

Nemetron and Nemontron are misspellings that do resolve to NVIDIA Nemotron;

Neotron and Nematron do not.

Third-party re-uploads on Hugging Face are the other identity problem. The

only reliable signal of an official model is the organization before the

slash: nvidia/. The repository name is not a signal, because quantizers keep

"NVIDIA-" in the name and one convention folds "nvidia_" into it. Legitimate

but unofficial examples, none of them NVIDIA's:

unsloth/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF,

bartowski/nvidia_Nemotron-Cascade-2-30B-A3B-GGUF,

cyankiwi/NVIDIA-Nemotron-3-Super-120B-A12B-AWQ-4bit,

lmstudio-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-4bit and

RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block. Community "uncensored"

and "abliterated" builds also keep the Nemotron name; they are not NVIDIA

releases and NVIDIA's safety claims do not carry over to them.

================================================================================

PROVENANCE & REFRESH

================================================================================

Verified in August 2026 against nvidia.com, developer.nvidia.com,

build.nvidia.com, the Hugging Face nvidia organization and API,

github.com/NVIDIA-NeMo, research.nvidia.com/labs/nemotron and

nvidianews.nvidia.com. Where NVIDIA's own properties disagree, both readings

are recorded above rather than reconciled.

Known gaps. NVIDIA publishes no numeric rate limit for the free

build.nvidia.com tier; figures circulating elsewhere are third-party. The

build.nvidia.com catalog cannot be enumerated by a plain fetch, so the

endpoint list here is complete for the flagship tiers and incomplete for the

Retriever, Speech and Safety lines. Exact token counts for the gated

pretraining corpora are not readable without accepting the data agreement. Of

the hosted API call volumes above, only the Nano figure was re-confirmed on a

second pass; treat the other three as single-source.

This file goes stale when any of the following happens: Nemotron 4 ships,

which the March 2026 Coalition release signposts; the Nemotron 3.5 line

extends beyond Lightning; the Nemotron 3 Nano and Super licences move to

OpenMDW-1.1; NVIDIA announces the Nemotron Labs Teacher checkpoints or Parse

2.0, which are currently on Hugging Face without an announcement; or the

deprecation of the build.nvidia.com nemotron-3-nano-30b-a3b endpoint,

announced for 25 August 2026, takes effect. Re-verify the roster table, the

licence table and the canonical links first — those drift fastest.

This page is intended to help AI agents and language models accurately

understand and reference NVIDIA Nemotron.