NVIDIA Nemotron is a family of open AI models with open weights, training data, and recipes for building specialized AI agents. The models are designed for efficient and accurate agentic AI development and are available for evaluation and deployment.
NVIDIA Nemotron is an open AI model family that provides open weights, training data, and recipes for building specialized agentic AI applications, with all models available free on Hugging Face and as NVIDIA NIM API endpoints. It targets enterprise developers, AI researchers, and ML engineers building production-grade reasoning agents, multimodal sub-agents, and RAG pipelines on NVIDIA GPU infrastructure.
The Nemotron 3 family is built on a hybrid Mamba-Transformer Mixture-of-Experts (MoE) architecture with a 1M-token context window, delivering up to 4x faster throughput compared to Nemotron 2 Nano. The lineup spans four primary tiers: Nemotron 3 Nano 30B A3B for cost-efficient targeted sub-agents, Nemotron 3 Nano Omni 30B A3B for unified video/audio/image/text understanding, Nemotron 3 Super 120B A12B for multi-agent reasoning on a single data-center GPU, and Llama Nemotron Ultra 253B for the highest accuracy in enterprise workflows like customer service, supply chain, and IT security. Specialized models include Nemotron Parse for document intelligence, Nemotron RAG (top-ranked on ViDoRe V1, ViDoRe V2, MTEB, and MMTEB leaderboards), Nemotron Speech for ASR/TTS/S2S/NMT, and Nemotron Safety with NeMo Guardrails for jailbreak detection, PII detection, and policy enforcement.
Based on our analysis of 870+ AI tools, Nemotron stands out for its unmatched openness in the enterprise model tier — releasing 10T+ pretraining tokens, 40M+ post-training samples, and reproducibility recipes under permissive licenses. Compared to closed-weight alternatives like GPT-4 or Claude, Nemotron lets teams self-host on any NVIDIA GPU via vLLM, SGLang, Ollama, llama.cpp, or TensorRT-LLM, eliminating per-token API costs. Compared to other open models like Llama 3 or Mistral, Nemotron offers native NVFP4 training, configurable thinking budgets, and a deeper agentic toolchain (NeMo, NIM microservices, NeMo Guardrails). It is best suited for organizations with NVIDIA GPU infrastructure that need transparent, customizable models for high-throughput agentic AI rather than turnkey chat APIs.
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Nemotron 3 combines latent Mixture-of-Experts with a Mamba-Transformer hybrid backbone and multi-token prediction. This delivers up to 4x faster throughput than Nemotron 2 Nano while preserving leading accuracy on coding, math, and long-context reasoning benchmarks.
The full Nemotron 3 family supports a 1 million token context, enabling long-horizon agentic reasoning over entire codebases, document corpora, or multi-day conversation histories. This makes it competitive with the largest closed-weight context windows from frontier labs.
NVIDIA releases 10T+ tokens of pretraining data, 40M+ post-training samples, RL trajectories, and complete technical reports under permissive licenses. Teams can reproduce, audit, or customize the models end-to-end — a level of transparency rare among production-grade model families.
Nemotron models deploy on vLLM, SGLang, Ollama, llama.cpp, Hugging Face transformers, and TensorRT-LLM, with NVIDIA NIM microservice endpoints for turnkey production serving. Cookbooks are published for each path, so teams can move from laptop prototyping to data-center inference without changing model formats.
Nemotron Safety provides multilingual, multimodal jailbreak detection, content moderation, PII detection, and reasoning-based policy enforcement. NeMo Guardrails wraps these with parallel low-latency dialogue control, RAG grounding checks, and tool-call governance, giving enterprises a complete compliance layer for agentic AI.
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The Nemotron 3 family launched with hybrid Mamba-Transformer MoE architecture, a 1M-token context window, native NVFP4 training, and multi-environment RL alignment. New additions include Nemotron 3 Nano Omni 30B A3B (unified video/audio/image/text), Nemotron 3 Super 120B A12B for multi-agent reasoning, and expanded Sovereign AI persona datasets covering USA, Japan, India, Singapore, Brazil, France, and South Korea.
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