Model
NVIDIA Nemotron 3
Openness
open_weights_recipe
Licence
NVIDIA Nemotron Open Model License (v. 2025-12-15)
You 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.
Do you really own it?
Partial
none·limited·partial·substantial·full
Analytical input: AOI C · 66/100
The four ownership factors
Floor-weighted, not averaged. Nothing is weak, but use & modify is only moderate, so it misses the bar for substantial - strong on both use & modify and data control - and lands at partial.
1
Use and modify freelyCan you run, modify and adapt it with no gate and no field-of-use trap?
ModerateThe NVIDIA Nemotron Open Model License is genuinely permissive - perpetual, royalty-free, IRREVOCABLE, commercial use allowed, no acceptable-use or field-of-use restriction, weights ungated and fine-tunable - which is a real improvement on the earlier reading. It is held at moderate, not strong, because it is Apache-2.0-derived but non-OSI: the termination reaches 'an output from the Work' (broader than Apache-2.0) and redistribution must carry the NVIDIA attribution notice, so it is an open_weight community licence with conditions, short of Ultra's unconditional OpenMDW 'deal without restriction' grant.
How this scores (AOI sub-dimensions)
Openness4/5how much is released - weights, data, code, licence - and how freelyOpen weights plus recipe: ungated safetensors weights, runnable training recipes on GitHub, a white paper, and post-training datasets genuinely under CC-BY-4.0 put Super and Nano at the open_weights_recipe tier - materially more open than the open-weights norm.
Legal3/5how permissive and clean the licence is for real commercial useA clean, commercially permissive grant for common uses: the NVIDIA Nemotron Open Model License is perpetual, royalty-free and IRREVOCABLE, with no acceptable-use or field-of-use restriction, terminating only on the licensee bringing IP litigation over the Work or an output from it - more permissive than the earlier aggregate reading claimed.
2
TransparencyDo you know what it is: weights, training, behaviour, and legible terms?
StrongUnusually 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.
3
ReliabilityIs it reliable and good enough for the job?
ModerateOperational is strong (4: broad serving, official quantizations, a small portable Nano), but safety is only 3: there is no published model-level safety evaluation or independent red-team, so misuse control is a gap you must fill yourself with the downloadable guard stack. Under the ownership rule a safety score of 3 caps this factor at moderate, however capable the models are.
How this scores (AOI sub-dimensions)
Operational4/5how practical it is to run, serve and maintain in productionBroad, mature serving story: vLLM, SGLang, Ollama, llama.cpp and NVIDIA's own NIM self-hosted microservices, with official BF16 / FP8 / NVFP4 quantizations and, crucially, a genuinely small Nano (30B / 3.5B active) that runs on modest hardware.
Safety3/5whether misuse risks are evaluated and guardrails are providedA safety-tuned release (Nemotron-SFT-Safety post-training data plus keyword/regex filtering) with a genuinely downloadable, multi-domain companion guard classifier (Nemotron-3-Content-Safety, 23 categories / 12 languages) and the Apache-2.0 NeMo Guardrails and garak tooling at the family level.
4
Doesn't extract your dataDoes running it keep your knowledge and data yours?
StrongSelf-hosted via NIM, the weights and inference run on your own infrastructure and data never leaves your enclave, and the licence claws back no rights - the grant is explicitly IRREVOCABLE, terminating only on the licensee's own IP litigation. That is the strong case for a self-hostable model.
How this scores
Not a scored AOI dimension. For a self-hosted model, data-control is a structural property of running the weights yourself, strong by default unless the model phones home or the licence claws back rights. For a hosted API this factor is the retention + train-on-inputs + residency read, scored from the binding terms.
How the AOI score is computed
The seven dimensions above, each scored 0 to 5, weighted and summed to the 0 to 100 headline. The score is the analytical input behind the ownership verdict, not the verdict itself.
DimensionScoreWeightPoints
Openness4/50.1814.4
Provenance3/50.169.6
Legal3/50.169.6
Safety3/50.169.6
Performance3/50.148.4
Operational4/50.129.6
Governance3/50.084.8
HeadlineC · 66/100
Sources
Every rating traces to a primary document. Read means the text was verified; unverified means it is known to exist but has not yet been read.
DocumentWhat it grounds
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.