Quick Summary
- NVIDIA Nemotron 3 Super is a 120-billion-parameter open model with only 12 billion active parameters at inference.
- It addresses two major bottlenecks in agentic AI: context explosion and the thinking tax.
- A hybrid mixture-of-experts (MoE) architecture delivers up to 5x higher throughput than the previous Nemotron Super model.
- A 1-million-token context window allows agents to hold full workflow state in memory without losing focus on their goals.
- The model runs in NVFP4 precision on NVIDIA Blackwell hardware, enabling up to 4x faster inference than FP8 on Hopper.
- NVIDIA is releasing the model with open weights, full training datasets, and a complete methodology under a permissive license.
- Nemotron 3 Super is already deployed by companies across software development, life sciences, cybersecurity, and enterprise automation.
NVIDIA Nemotron 3 Super Is Built for the Agentic AI Era
NVIDIA Nemotron 3 Super represents a meaningful shift in how large language models are designed for real-world AI agents. Rather than optimizing purely for raw capability, this 120-billion-parameter open model is built to run complex multi-agent workflows efficiently and at scale. It addresses two well-documented obstacles that have slowed the move from chatbots to autonomous systems. The result is a model that raises the performance bar while actually lowering the cost of running intelligent agents in production.
The Two Problems Slowing Agentic AI
As organizations push beyond simple conversational interfaces, they run into structural limits that standard models were never designed to solve. Two problems stand out above the rest.
The first is context explosion. Multi-agent workflows can generate up to 15x more tokens than ordinary chat interactions, because each step requires passing along full histories, tool outputs, and intermediate reasoning. As these histories grow, costs rise and agents can begin to drift from their original goals.
The second problem is what researchers sometimes call the thinking tax. Sophisticated agents reason at every step of a task. When every subtask calls a large, expensive model, the total compute cost quickly makes practical deployment difficult.
Nemotron 3 Super was designed specifically to address both of these constraints. It offers a 1-million-token context window, which allows agents to hold an entire workflow in memory without needing to compress or discard earlier context. This prevents the kind of goal drift that emerges when agents can no longer “see” where they started.
Hybrid Architecture Explained in the NVIDIA Nemotron 3 Super Model
The performance gains in Nemotron 3 Super come from a combination of architectural choices working together, not from any single design decision.
The model uses a hybrid mixture-of-experts (MoE) structure that blends Mamba layers and transformer layers. Mamba layers deliver roughly 4x better memory and compute efficiency, while transformer layers handle the deeper reasoning tasks. Together, they allow the model to be both fast and accurate.
Only 12 billion of the model’s 120 billion parameters are active at inference time. This makes each forward pass far less expensive than running a dense model of comparable overall size. A new technique called Latent MoE improves accuracy further by activating four expert specialists for the computational cost of one during token generation.
The model also uses multi-token prediction, which means it generates several tokens at once rather than one at a time. This alone contributes to 3x faster inference in practice. When combined with NVFP4 precision on the NVIDIA Blackwell platform, the model runs up to 4x faster than FP8 inference on the previous Hopper generation, with no reported loss in accuracy.
On independent benchmarks, Nemotron 3 Super has taken the top spot on Artificial Analysis for efficiency and openness among models of comparable size. The model also powers the NVIDIA AI-Q research agent, which currently holds the number one position on both DeepResearch Bench and DeepResearch Bench II. These benchmarks measure the ability to conduct thorough, multi-step research across large document sets while maintaining coherent reasoning throughout.
NVIDIA Nemotron 3 Super Releases Open Weights and Training Data
NVIDIA is making Nemotron 3 Super available as an open model under a permissive license. Developers can download the weights and deploy the model on workstations, in private data centers, or through cloud infrastructure.
The openness extends beyond just the weights. NVIDIA is publishing the complete training methodology, including more than 10 trillion tokens of pre-training and post-training datasets. The release also includes 15 reinforcement learning environments and full evaluation recipes. Researchers who want to fine-tune the model or use it as a foundation for their own systems can work within the NVIDIA NeMo platform to do so.
The model was trained on synthetic data produced using frontier reasoning models, which reflects a broader trend toward using AI-generated data to improve the quality and diversity of training sets.
How NVIDIA Nemotron 3 Super Fits Into Agentic Systems
Nemotron 3 Super is not designed to be a single-purpose model. It is built to handle complex subtasks inside larger multi-agent pipelines, where reliability and context retention matter as much as raw output quality.
In software development workflows, a coding agent can load an entire codebase into context at once. This enables end-to-end code generation and debugging without splitting the project into separate document chunks. Several companies in this space have already begun integrating the model. CodeRabbit and Greptile are both using it in code review agents alongside proprietary models, with the goal of achieving higher accuracy at a lower per-query cost.
In financial analysis, the model’s long context window allows it to ingest thousands of pages of reports without needing to re-reason across the same material repeatedly. This improves efficiency and reduces the chance of compounding errors across a long reasoning chain.
Tool calling accuracy is another area where the model has been optimized. Autonomous agents often need to navigate large function libraries to complete tasks, and a single execution error in a high-stakes environment can have real consequences. Nemotron 3 Super has been benchmarked to reliably select and call the correct tools, which is particularly valuable in domains like cybersecurity orchestration.
Perplexity is offering users access to the model for search and as one of 20 models inside its Computer product. On the enterprise side, organizations including Amdocs, Palantir, Siemens, Cadence, and Dassault Systèmes are deploying and customizing the model to automate workflows across telecom, semiconductor design, and manufacturing. Life sciences organizations like Edison Scientific and Lila Sciences are also using it for deep literature search and molecular understanding tasks.
Availability and Deployment Options
Nemotron 3 Super is available now through several channels. Developers can access it directly at build.nvidia.com, through Perplexity, on OpenRouter, and via Hugging Face.
Cloud service providers are supporting the rollout as well. The model is currently available on Google Cloud Vertex AI and Oracle Cloud Infrastructure, with availability on Amazon Bedrock and Microsoft Azure expected in the near future.
For on-premise deployments, Dell Technologies is bringing the model to the Dell Enterprise Hub on Hugging Face, optimized for the Dell AI Factory. HPE is also adding Nemotron to its agents hub for enterprise use. Inference providers including Cloudflare, Fireworks AI, Together AI, and Baseten are among those offering managed access.
The model is packaged as an NVIDIA NIM microservice, which makes it straightforward to integrate into both cloud and on-premises AI infrastructure without significant overhead.
Conclusion
The release of NVIDIA Nemotron 3 Super signals a practical maturation in how the AI industry thinks about agentic infrastructure. It does not just push benchmark scores higher. It addresses the specific mechanical problems that have made deploying multi-agent systems expensive and unreliable in real environments. By combining open weights, a transparent training methodology, and an architecture designed for long-context reasoning, NVIDIA is giving both researchers and enterprise teams a capable foundation to build on. For anyone working in agentic AI, this model is worth a close look.
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