Nvidia Nemotron and Nemo Fuel Sovereign AI Builds

Nvidia has pushed further into "sovereign AI" - the idea of keeping models, data, and inference under your own control. Nemotron is Nvidia's open-weight model family and NeMo is its open-source toolkit for training, tuning, evaluating, and serving models. If you want to run private large language models (LLMs) on your own hardware, this combination is meant for you.

What's what:

• Nemotron = models: Example models include Nemotron-4-340B-Instruct and Nemotron-3 Nano 30B. These come as open weights under the Nvidia Open Model License (commercial use and derivatives allowed). You can download weights on Hugging Face or NGC after accepting the license. For details see huggingface.co and investor.nvidia.com.

• NeMo = tooling: NeMo is Nvidia's generative AI framework for training, fine-tuning, evaluating, and serving models. It is published under the Apache-2.0 license (Apache License 2.0) on GitHub and includes full documentation. Nemotron models are trained and served using NeMo and can be customized with NeMo Aligner. See github.com for the code and docs.nvidia.com for deployment guidance.

Availability and where to get it:

• Announcement/source: Nvidia announced Nemotron 3 on Dec 15, 2025. Nemotron-3 Nano is already available on Hugging Face and via hosted providers; larger sizes will roll out through the first half of 2026 and via Nvidia NIM microservices. See the press release at investor.nvidia.com.

• Example checkpoints and hardware: Running Nemotron-4-340B-Instruct for bfloat16 (BF16) inference needs heavy hardware: roughly 8x H200, 16x H100, or 16x A100 80GB GPUs. The Nemotron-3 Nano 30B targets much lighter setups and is available in BF16 (bfloat16) and quantized variants for smaller hardware. See the model listing at huggingface.co.

Licensing and compliance:

• Model and toolkit licenses: Nemotron models use the Nvidia Open Model License; NeMo is under Apache-2.0. Both allow commercial use. Nvidia does not claim ownership of your model outputs. Enterprise deployment is supported via Nvidia NIM microservices. More licensing details are at nvidia.com.

Sovereign AI, translated:

• Keep your training data, model weights, and inference on-premises or in a specific jurisdiction to meet data residency and regulatory needs (examples: GDPR in Europe, HIPAA for health data in the U.S., or public-sector rules). That is the main purpose of offering open weights and tooling you can host yourself. See Nvidia's explainer at blogs.nvidia.com.

Founder playbook (fast, not reckless):

• Scope: Start with models in the 7B to 30B parameter class. Don't try to train a 340B model from scratch on day one.

• Infra: Budget for A100/H100-class GPUs (these are data-center Nvidia GPUs). Very large models like 340B need multi-node clusters; a 30B model can often be fine-tuned on a single high-VRAM (video RAM) GPU.

• Stack: Use NeMo for training, fine-tuning, and alignment. Deploy with Nvidia NIM microservices or a DIY inference engine such as vLLM (an open-source inference library) if you run your own servers.

• Governance: Put data-residency controls, logging policies, and model-risk checks in place before you scale. Regulators will expect clear records and safeguards.

Bottom line:

Nemotron gives you open weights; NeMo gives you the wrench set to train and deploy them. If you need control over data, performance, and latency, this is a practical option - not just marketing. For sources and hardware details, see the official Nvidia announcement at investor.nvidia.com.