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Support · NVIDIA Nemotron 3

Will it last?

Ownership levelPartialnone·limited·partial·substantial·fullAnalytical input C · 66/100

This page is a projection of the one entry record, the Transparency factor that Support covers. The full verdict is set by all four factors together, floor-weighted so the weakest caps the whole.

Which domain expands which factor

AssessUse & modify + Transparency
ImplementData control + Reliability
UseReliability
SupportTransparency

Common problems & fixes

Aggregated pitfalls for a hybrid Mamba-Transformer MoE pair: kernel-to-engine version mismatches for the Mamba-2 and MoE layers, MoE sharding and multi-GPU memory for Super, NVFP4/FP8 quantization-engine compatibility, and revision drift on download. Match engine and kernel versions to each variant's documented stack, pin the revision, and verify checksums. This is not an exhaustive catalogue.

Versions, changelog & cadence

Nemotron 3 Super and Nano were released around 2025-12 on the verified nvidia org, each with BF16, FP8 and NVFP4 variants. There is no single-checkpoint changelog; the Hugging Face revision hash is the anchor, so pin it.

Security / vulnerability disclosure

NVIDIA maintains a general corporate product-security reporting path (nvidia.com/en-us/security/). A model-specific vulnerability-disclosure policy for Nemotron was not confirmed this pass, so treat model-level issue reporting as going through the Hugging Face and GitHub org presence.

Community & support channels

Support is the NVIDIA open-model presence: the nvidia Hugging Face org (model discussions), the NVIDIA-NeMo GitHub organisation (the Nemotron recipes and NeMo Guardrails), and the NVIDIA/garak repository for red-team and safety-tooling questions.

Tracked known issues

From the cards, recipes and licence: the partial-corpus non-reproducibility and the absence of a published model-level safety evaluation are the recurring caveats. Track these against your own control stack and red-team rather than a published issue tracker.

How this scores

The ownership factor this domain covers, drawn from the one entry record.

2

TransparencyDo you know what it is: weights, training, behaviour, and legible terms?

Strong

Unusually inspectable for the registry: ungated weights, a white paper, runnable training recipes, and partially released post-training data under CC-BY-4.0 let you see a great deal of how the model was built (open_weights_recipe tier). The limit is reproducibility, not visibility - the full corpus is not released - which caps the openness tier, not transparency.

How this scores (AOI sub-dimensions)

Provenance3/5how well we can trace and verify what went into the modelDistributed from the verified nvidia org on Hugging Face as ungated safetensors with official BF16 / FP8 / NVFP4 quantization variants and a clear canonical source (checklist ~4/8).

Governance3/5how accountable and well-documented the publisher isA reputable, accountable publisher (NVIDIA) with a white paper, a verified hub presence and an active release cadence, and a general corporate security-reporting path.

What this means for adoptionYou get unusually strong transparency for an open model - ungated weights, a white paper, runnable recipes and CC-BY post-training data - and, on the corrected licence reading, a clean irrevocable grant with no field-of-use restriction, so self-hosting keeps your data and rights yours. Ownership stays partial, not substantial, because the corpus is only partly released (not reproducible), the non-OSI licence with its attribution notice and output-reaching termination holds use-and-modify at moderate, and the absence of a published model-level safety evaluation holds reliability at moderate. Self-host via NIM, assemble the downloadable guard stack, and red-team for your own use case.

Sources

The same evidence records as the entry sheet. Read means the text was verified; unverified means it is known to exist but not yet read.

Model cardread2026-08-03

Nemotron 3 Super and Nano model cards on the verified nvidia HF org: Super 120B/12B, Nano 30B/3.5B active; hybrid Mamba-Transformer LatentMoE + MTP; up to 1M context; ~25T training tokens; ungated safetensors with BF16/FP8/NVFP4 variants; both cards state "a family of open models with open weights, training data, and recipes"; licence tag nvidia-nemotron-open-model-license.

Licenceread2026-08-03

NVIDIA Nemotron Open Model License (v.

Documentationread2026-08-03

Training data + recipes are PARTIALLY released: post-training datasets (Nemotron-Post-Training-v3, e.g.

Technical_reportread2026-08-03

Nemotron 3 White Paper (arXiv 2512.20856), read (abstract): hybrid Mamba-Transformer LatentMoE, up to 1M context, NVFP4 training for Super/Ultra.

Model cardread2026-08-03

Safety, read: no published model-level safety evaluation or independent red-team for Super/Nano (base cards carry only a keyword/regex data-filter note; white paper safety section is a contributor list).

Documentationread2026-08-03

Verified nvidia org on Hugging Face; Super and Nano weight repos are ungated safetensors with published BF16/FP8/NVFP4 quantization variants; some training DATASETS are gated but the WEIGHTS are not.

Terms of serviceread2026-08-03

NVIDIA NIM is self-hosted deployment tooling ("data never leaves your secure enclave"), so NVIDIA is not an AOI inference-provider entry and self-hosting supports data control.

Third-party analysisread2026-08-03

NVIDIA is NOT a signatory to the EU GPAI Code of Practice (EU signatory list), and no NVIDIA training-content summary or copyright policy for Nemotron was located on the NVIDIA trust centre.