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NVIDIA released Nemotron 3 yesterday—and the model itself might not be the most important part of the announcement.
Yes, there's a new family of models (Nano, Super, Ultra) with a hybrid mixture-of-experts architecture. Yes, the smallest model delivers 4x throughput compared to its predecessor. But the strategic move is everything NVIDIA released alongside it: 3 trillion tokens of training data, the RL environments used to train the models, and the post-training infrastructure—all under the NVIDIA Open Model License.
As one analyst put it: "This is NVIDIA's response to DeepSeek disrupting the AI market. They're offering a business-ready open alternative with enterprise support and hardware optimization."
The Nemotron 3 Model Family
Three models, each targeting different compute budgets and complexity requirements:
Nemotron 3 Nano — 30B parameters with only 3B active at any time. Available now. Optimized for tasks like software debugging, content summarization, AI assistant workflows, and information retrieval. This is the one you can download from Hugging Face today.
Nemotron 3 Super — ~100B parameters with 10B active. Designed for multi-agent applications and high-accuracy reasoning. Coming H1 2026.
Nemotron 3 Ultra — ~500B parameters with 50B active. The large reasoning engine for complex planning and research workflows. Also H1 2026.
The architecture is what makes the efficiency claims credible. Nemotron 3 uses a hybrid latent mixture-of-experts (MoE) combined with a Mamba-Transformer design. The MoE approach means only relevant parameters activate for each token—dramatically reducing compute while maintaining capability.
NVIDIA's Kari Briski explained the latent MoE innovation: "All these experts in your model share a common core and keep only a small part private. It's kind of like chefs sharing one big kitchen, but they each have their own spice rack."
Key specs for Nano: 1 million token context window (7x larger than Nemotron 2), 4x higher throughput than predecessor, 60% reduction in reasoning token generation. Supports reasoning ON/OFF modes—multi-step reasoning when needed, concise responses when not.
The Real Story: Open RL Infrastructure
What sets this release apart is what NVIDIA is giving away. The model weights are almost table stakes at this point—Meta, Mistral, and others have been releasing capable open models for years. But NVIDIA is releasing the training infrastructure itself.
NeMo Gym is an open-source library for building reinforcement learning environments. This is the framework NVIDIA used to post-train Nemotron 3. It decouples RL environments from the training loop itself, letting you create and scale training instances independently.
Why does this matter? Pre-training teaches models to predict tokens, but that's not the same as completing domain-specific tasks. Traditional RLHF (reinforcement learning from human feedback) doesn't scale for complex agentic behaviors because you can't get enough human ratings fast enough.
NeMo Gym enables "Reinforcement Learning from Verifiable Rewards" (RLVR)—computational verification of task completion rather than subjective human ratings. The model runs through environments that evaluate sequences of actions: generating correct tool calls, writing functional code, producing multi-step plans that satisfy verifiable criteria.
NeMo RL is the high-performance training engine. It supports GRPO, DAPO, SFT, DPO, and on-policy distillation algorithms. Features end-to-end FP8 training and async RL. The reproducible training code for Nemotron 3 Nano is now on GitHub.
NeMo Evaluator handles safety and performance validation.
What this means: Meaningful RL training for large models has historically been accessible only to major AI labs. NeMo Gym and NeMo RL lower that barrier significantly. Teams can now run RL post-training on their own infrastructure with the same tools NVIDIA used internally.
3 Trillion Tokens of Training Data
NVIDIA released the actual training data—not just the model weights. This includes:
- Nemotron pretraining data — The corpus used for initial model training
- Nemotron-post-training 3.0 — 13 million samples for supervised fine-tuning and RL alignment
- Nemotron-RL datasets — Curated RL datasets for tool-use, planning, and multi-step reasoning
- Nemotron Agentic Safety Dataset — Nearly 11,000 labeled traces from realistic, tool-using workflows
The safety dataset is particularly notable. As models become agents that take actions in the real world, traditional safety evaluation falls short. This dataset provides real-world telemetry from agentic workflows to help teams evaluate emerging risks before deployment.
All available on GitHub and Hugging Face under the NVIDIA Open Model License (commercial use permitted).
The Strategic Positioning
NVIDIA isn't trying to compete with OpenAI or Anthropic's hosted services. They're positioning as the infrastructure layer for enterprises that want to build and own their own AI agents.
The target buyer: organizations that need deployment flexibility and can't accept vendor lock-in. Regulated industries like healthcare, finance, and defense require auditable, on-premises alternatives. Government customers need models aligned with their own data, regulations, and values.
Briski addressed this directly: "Many of our enterprise customers cannot deploy certain models or build their business on models with opaque source codes."
There's also the geopolitical angle. US export restrictions on Chinese AI amplify NVIDIA's advantage for organizations needing sovereign AI capabilities. Countries from Japan to South Korea to Saudi Arabia are adopting Nemotron for this reason.
The timing matters too. Meta's Llama growth reportedly stalled after April's Llama 4 launch. DeepSeek showed that efficiency breakthroughs could come from outside the usual players. NVIDIA is positioning Nemotron as the trusted enterprise alternative.
Benchmark Performance and Limitations
According to Artificial Analysis, Nemotron 3 Nano ranks among the most open models while landing in the upper tier for intelligence—a combination that remains rare.
The model excels in instruction following, math, video understanding with temporal reasoning, function calling for task automation, and UI interaction capabilities. Strong code generation and practical software engineering problem-solving.
But let's be clear about limitations: Claude and GPT-4o still outperform Nemotron 3 on specialized tasks like coding benchmarks. This isn't meant to be a frontier model that beats everything—it's meant to be an efficient, transparent model that handles most production workloads at lower cost.
The Super and Ultra models (coming H1 2026) will incorporate latent MoE for greater expert specialization, multi-token prediction for higher inference throughput, and NVFP4 4-bit precision for training on Blackwell hardware.
Availability and Ecosystem
Nemotron 3 Nano is available now on Hugging Face and through inference providers including Baseten, DeepInfra, Fireworks, FriendliAI, OpenRouter, and Together AI. Also available as a pre-built NVIDIA NIM microservice.
Coming soon: AWS via Amazon Bedrock, Google Cloud, CoreWeave, and Microsoft Foundry.
Framework support includes llama.cpp, SGLang, and vLLM. Prime Intellect and Unsloth are integrating NeMo Gym directly into their workflows.
Early adopters include Accenture, Cadence, CrowdStrike, Cursor, Deloitte, EY, Oracle Cloud Infrastructure, Palantir, Perplexity, ServiceNow, Siemens, Synopsys, and Zoom.
Perplexity CEO Aravind Srinivas: "With our agent router, we can direct workloads to the best fine-tuned open models, like Nemotron 3 Ultra."
What This Means for Practitioners
The shift from single chatbots to multi-agent systems creates new infrastructure requirements. Communication overhead, context drift, and inference costs all compound when you're orchestrating dozens of specialized agents.
Nemotron 3's hybrid MoE architecture directly addresses inference costs. The 1 million token context window handles the coherence problem. The open RL infrastructure lets you customize agents for your specific domain.
Practical next steps:
- If you're building multi-agent systems, benchmark Nemotron 3 Nano against your current model choices. The throughput claims are significant.
- If you need to customize models for domain-specific agentic tasks, explore NeMo Gym. The RL environments are production-grade.
- If you're in a regulated industry or have data sovereignty requirements, Nemotron 3's openness and auditability may solve compliance blockers.
- If you're building agent safety evaluation systems, the Agentic Safety Dataset provides a foundation that didn't exist before.
Jensen Huang framed the release: "Open innovation is the foundation of AI progress. With Nemotron, we're transforming advanced AI into an open platform that gives developers the transparency and efficiency they need to build agentic systems at scale."
The model matters. But the infrastructure matters more.
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