NVIDIA Nemotron 3 Nano: A 30B Open Model That Activates Only 3B Parameters Per Token

NVIDIA Enters the Open Model Race With an Efficiency Play

NVIDIA debuted the Nemotron 3 family of open models in December 2025, with Nemotron 3 Nano as the first available release. The model represents a departure from the brute-force scaling approach that has dominated the large language model landscape. Instead of maximizing total parameter count, Nemotron 3 Nano uses a hybrid mixture-of-experts architecture that activates only 3 billion parameters out of a total 30 billion on each forward pass. The result is a model that delivers 4x higher token throughput than its predecessor, Nemotron 2 Nano, while maintaining competitive accuracy.

The model weights are released under the NVIDIA Open Model License, and NVIDIA has also published nearly 10 trillion tokens of its synthetic pretraining corpus, along with detailed training and post-training recipes on GitHub and Hugging Face. This level of openness is notable for NVIDIA, a company historically focused on hardware rather than model distribution.

Architecture: Mamba, Transformers, and MoE in One Model

Nemotron 3 Nano's architecture is its most distinctive feature. It combines three computational paradigms in a single model: Mamba layers for efficient sequence modeling, Transformer layers for precision reasoning, and mixture-of-experts (MoE) routing for scalable compute efficiency.

The architecture alternates Mamba-2/MoE pairs with sparse self-attention layers in a carefully designed pattern. Mamba layers handle long-range sequence dependencies efficiently, avoiding the quadratic attention cost that constrains standard Transformers on long contexts. The sparse self-attention layers activate selectively for tasks that require precise cross-token reasoning. The MoE routing system directs each token to a subset of specialized experts, ensuring that only 3B of the model's 30B total parameters fire on any given inference step.

This hybrid approach yields a practical benefit: the model supports a native 1-million-token context window while maintaining throughput that makes it viable for multi-agent deployments where multiple model instances run concurrently. NVIDIA specifically designed the architecture for DGX Spark, H100, and B200 GPUs, optimizing memory access patterns for its own hardware ecosystem.

Training Methodology and Open Data

Nemotron 3 Nano was pretrained on nearly 10 trillion tokens of synthetic data generated through NVIDIA's augmentation pipelines. Post-training used a 13-million-sample corpus for supervised fine-tuning and reinforcement learning.

The reinforcement learning phase is particularly noteworthy. NVIDIA employed multi-environment RL through NeMo Gym, a framework that exposes the model to diverse agentic scenarios including tool use, multi-step reasoning, and code execution. The training emphasis on "real agentic behavior" through trajectory-based optimization distinguishes Nemotron 3 from models that rely primarily on instruction tuning for task completion.

NVIDIA has published the pretraining corpus, post-training data, and complete training recipes. Developers can inspect, modify, and reproduce the entire pipeline, enabling customization for domain-specific applications. This reproducibility commitment extends to NeMo RL and NeMo Evaluator, the reinforcement learning and evaluation toolkits released alongside the model.

Benchmark Performance and Efficiency Metrics

Nemotron 3 Nano achieves a score of 52 on the Artificial Analysis Intelligence Index v3.0, which Artificial Analysis ranks as leading accuracy among similarly sized models. The model's efficiency story is equally compelling: it delivers the highest throughput among small language reasoning models while reducing reasoning-token generation by up to 60% compared to models that use chain-of-thought approaches without MoE routing.

The throughput advantage is critical for agentic AI applications where multiple model calls happen in rapid succession. A coding agent that needs to analyze code, generate tests, and review results requires fast sequential inference. A customer service system running multiple concurrent conversations needs high throughput per GPU. Nemotron 3 Nano's 4x throughput improvement over Nemotron 2 Nano directly translates to lower per-query cost in these deployments.

NVIDIA has also released an NVFP4 quantized checkpoint that further reduces memory requirements for inference on Blackwell architecture GPUs, enabling deployment on smaller hardware configurations without significant accuracy loss.

The Nemotron 3 Family Roadmap

Nemotron 3 Nano is the smallest member of a three-model family. Nemotron 3 Super, with approximately 100 billion total parameters and 10 billion active per token, and Nemotron 3 Ultra, at approximately 500 billion total parameters with 50 billion active, are scheduled for release in the first half of 2026.

The Super and Ultra models will introduce two additional architectural innovations. Latent MoE enables 4x more experts at the same inference cost through latent space routing, improving semantic specialization. Multi-token prediction allows the model to predict multiple future tokens simultaneously, improving accuracy by approximately 2.4% during training while enabling speculative decoding speedups at inference time.

Both larger models will use NVIDIA's NVFP4 4-bit floating-point training format on the Blackwell architecture, reducing training memory requirements while maintaining accuracy. This positions the Nemotron 3 family as a showcase for NVIDIA's hardware-software co-optimization strategy.

Deployment and Ecosystem

Nemotron 3 Nano is available through multiple channels: Hugging Face for direct download, inference providers including Baseten, DeepInfra, Fireworks, Together AI, and OpenRouter for API access, and NVIDIA NIM microservices for enterprise deployment. The breadth of deployment options lowers the barrier to adoption for developers who may not have direct access to NVIDIA DGX hardware.

The model targets specific use cases: software debugging, content summarization, AI assistant workflows, information retrieval, multi-agent collaboration, and domain-specialized agent development. NVIDIA positions Nemotron 3 Nano not as a general-purpose chatbot competitor but as an inference engine for structured agentic workflows where throughput and efficiency matter more than raw benchmark scores.

Competitive Positioning

Nemotron 3 Nano competes with models like Meta's Llama 4 Scout, Alibaba's Qwen 3.5, and Mistral's smaller models. Its differentiation is architectural: the hybrid Mamba-Transformer-MoE design offers a fundamentally different efficiency profile than the dense Transformer models that dominate the open-weight landscape.

NVIDIA's unique position as both a hardware manufacturer and a model provider creates strategic alignment that other model builders cannot replicate. Nemotron 3 is optimized for NVIDIA GPUs at the silicon level, with memory access patterns and compute kernels designed for specific GPU architectures. This vertical integration means that Nemotron 3 running on NVIDIA hardware will likely outperform the same parameter count from competitors on the same hardware.

The open release of training data and recipes also serves a strategic purpose. By enabling the community to build on Nemotron 3, NVIDIA creates an ecosystem of fine-tuned models that run best on NVIDIA hardware, reinforcing GPU demand regardless of which specific model customers ultimately deploy.

Conclusion

Nemotron 3 Nano is a strategically significant release that demonstrates NVIDIA's ability to compete in the model layer while reinforcing its hardware advantage. The hybrid Mamba-Transformer-MoE architecture delivers genuine efficiency gains, the 1M-token context window and 4x throughput improvement address real deployment needs, and the open release of data and training recipes sets a high bar for reproducibility. For developers building agentic AI systems that require high throughput, low latency, and efficient GPU utilization, Nemotron 3 Nano is a compelling option. The upcoming Super and Ultra releases will determine whether NVIDIA's architectural approach scales to frontier-level performance.