Model
NVIDIA Nemotron 3 Nano Omni
Licence
NVIDIA Open Model Agreement (v. 2026-03-09)
Nano Omni gives you a small, portable, natively multimodal model under a clean irrevocable licence, and self-hosting keeps your data and rights yours - so data control is strong. Ownership stays partial because use-and-modify is only moderate (a non-OSI third licence with an unverified date and a public-subset-only recipe), transparency is only moderate (open_weights, not open_weights_recipe: the card does not release the data or advertise the recipe), and reliability is moderate (no model-level safety evaluation over a wider multimodal misuse surface). Self-host, assemble the multimodal guard stack, red-team the video/audio/image input path, and confirm the licence version against the PDF.
Do you really own it?
Partial
none·limited·partial·substantial·full
Analytical input: AOI C · 60/100
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 Open Model Agreement is genuinely permissive - perpetual, royalty-free, IRREVOCABLE, commercial use allowed, no acceptable-use or field-of-use restriction, weights ungated and fine-tunable. 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), it is a distinct third family licence whose on-document date is unverified against the PDF, and the released recipe surfaces only the public subset of the alignment corpus, so this is an open_weight community licence with conditions, short of Ultra's unconditional OpenMDW grant.
How this scores (AOI sub-dimensions)
Openness3/5how much is released - weights, data, code, licence - and how freelyOpen weights, a model card and a clear licence, but the openness posture is weaker than the rest of the family.
Legal3/5how permissive and clean the licence is for real commercial useA clean, commercially permissive grant: the NVIDIA Open Model Agreement is Apache-2.0-derived, perpetual, royalty-free and IRREVOCABLE, with commercial use allowed and no Trustworthy-AI, acceptable-use or guardrail clause (their absence is confirmed), terminating only on the licensee bringing IP litigation over the Work or an output from it.
2
TransparencyDo you know what it is: weights, training, behaviour, and legible terms?
Moderateopen_weights, not open_weights_recipe: the weights are ungated and inspectable and there is a model card and licence, but the card does not carry the family's open-data/recipe self-description, links only an image-training dataset, and the recipe that exists surfaces only the public subset. You can see the weights, but the data and full recipe are not clearly released - the anchor for moderate, not strong.
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 an official BF16 variant and a clear canonical source (checklist ~4/8).
Governance3/5how accountable and well-documented the publisher isA reputable, accountable publisher (NVIDIA) with 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 3 (runs on mainstream stacks with an official BF16 variant, but the multimodal serving path is narrower and the quantization coverage thinner) and safety is 3 (no model-level safety evaluation over a wider multimodal misuse surface). Under the ownership rule, a safety score of 3 caps this factor at moderate.
How this scores (AOI sub-dimensions)
Operational3/5how practical it is to run, serve and maintain in productionRuns on mainstream stacks (vLLM, SGLang, NVIDIA NIM) with an official BF16 variant and a linked TensorRT-LLM deploy cookbook, and at 31B / ~3B active it is genuinely portable.
Safety3/5whether misuse risks are evaluated and guardrails are providedA safety-tuned reasoning release with the downloadable family guard stack, whose Nemotron-3-Content-Safety classifier is multimodal (text and images) and so is partly attributable to this model, plus Apache-2.0 NeMo Guardrails and garak.
4
Doesn't extract your dataDoes running it keep your knowledge and data yours?
StrongSelf-hosted via NIM (or the linked TensorRT-LLM path), the weights and inference run on your own infrastructure and data never leaves your enclave, and the NVIDIA Open Model Agreement 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
Openness3/50.1810.8
Provenance3/50.169.6
Legal3/50.169.6
Safety3/50.169.6
Performance3/50.148.4
Operational3/50.127.2
Governance3/50.084.8
HeadlineC · 60/100
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 Nano Omni model card on the verified nvidia HF org (nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16, no "NVIDIA-" prefix): 31B total / ~3B active; Mamba2-Transformer hybrid MoE; multimodal (Video, Audio, Image, Text in / Text out); 256k context; ~717B training tokens; licence tag nvidia-open-model-agreement.
Licenceread2026-08-03
NVIDIA Open Model Agreement (v.
Documentationread2026-08-03
A training recipe for Nano Omni exists in the NeMo repo (github.com/NVIDIA-NeMo/Nemotron docs/nemotron/omni3, SFT then RL), read: caveat "The 20 RL datasets / 25 environments / ~2.3M rollouts referenced in the release blog compose the full upstream alignment corpus; this recipe surfaces the public/open-source subset." The card does not advertise this recipe, so on card signals the model is classified open_weights, with a note that a recipe exists.
Model cardread2026-08-03
Safety, read: no dedicated model-level safety evaluation for Nano Omni; safety is post-training data curation only, over a wider multimodal (video/audio/image) misuse surface.
Documentationread2026-08-03
Verified nvidia org on Hugging Face; the Nano Omni weight repo is ungated safetensors with an official BF16 variant.
Terms of serviceread2026-08-03
NVIDIA NIM is self-hosted deployment tooling ("data never leaves your secure enclave"), and the Nano Omni card links a TensorRT-LLM deploy cookbook, so self-hosting supports data control and NVIDIA is not an AOI inference-provider entry.
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.