Implement · NVIDIA Nemotron 3 Nano Omni

Can you run it?

Ownership levelPartialnone·limited·partial·substantial·fullAnalytical input C · 60/100

This page is a projection of the one entry record, the Reliability and Data control factors that Implement 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

Install & run

Download the ungated safetensors from the verified nvidia org on Hugging Face - note the repo id has no "NVIDIA-" prefix (nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16) - and serve with vLLM, SGLang or NVIDIA NIM; a TensorRT-LLM deploy cookbook is linked on the card. Pin the exact revision and verify checksums before loading.

Hardware & VRAM requirements

Small and portable: 31B total / ~3B active runs on modest single-GPU hardware, and an official BF16 variant is published. Budget additionally for the multimodal input pipeline (image/audio/video preprocessing). Exact per-GPU VRAM is not restated here.

Serving stacks

vLLM, SGLang and NVIDIA's own NIM self-hosted microservices, plus a linked TensorRT-LLM deploy cookbook. NIM runs on your own infrastructure, so data stays in your enclave. The multimodal serving path is less mature than the text-only members of the family.

Safe-deployment controls & Deployment Ceiling

Deployment Ceiling: T2 (bounded), conditional. The multimodal input surface widens the misuse path and there is no model-level safety evaluation, so the controls are your responsibility: the multimodal-capable Nemotron-3-Content-Safety guard classifier (text and images), the NeMo Guardrails toolkit, and the garak red-team scanner. Red-team the video/audio/image input path for your own use case before any autonomous use. The irrevocable licence imposes no field-of-use restriction. Complete the pre-deployment gate in the safe-deployment playbook.

Available quantizations

An official NVIDIA BF16 variant is published. A broader official FP8/NVFP4 program like the text models' was not confirmed for Nano Omni this pass, so treat lower-precision paths as community or runtime options until verified.

Fine-tuning & adaptation

A training recipe exists in the NeMo repo (docs/nemotron/omni3, SFT then RL), so adaptation is realistic. Reproduction is not: the recipe surfaces only the public subset of the alignment corpus (the full upstream corpus of 20 RL datasets / 25 environments / ~2.3M rollouts is not open). Adapt from the released checkpoint rather than expecting a full re-run.

API / OpenAI-compatible integration

Serve behind vLLM/SGLang or NVIDIA NIM for OpenAI-compatible endpoints; the multimodal input path may need the documented preprocessing. A hosted build.nvidia.com preview API exists for evaluation only: it reserves use of content "to improve NVIDIA products and services, including AI models".

How this scores

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

ReliabilityIs it reliable and good enough for the job?

Moderate

Operational 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.

Doesn't extract your dataDoes running it keep your knowledge and data yours?

Strong

Self-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.

What this means for adoptionNano 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.

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 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.