NVIDIA didn't stop at making tools; it has stepped in to build the "car" itself. On March 11, 2026, on the eve of GTC, NVIDIA officially released Nemotron 3 Super, an open‑source large language model with 120B total parameters (12B active parameters). The model adopts a hybrid Mamba‑Transformer‑MoE (LatentMoE) architecture, optimized for multi‑agent (Agentic AI) applications, and achieves industry‑leading inference speed, throughput, and complex task handling. NVIDIA's official tech blog and the Hugging Face model page state that Nemotron 3 Super performs close to or in some cases surpasses GPT‑5.4 on several Agentic benchmarks, and that weights, datasets, and training recipes are fully open‑sourced for developers. This marks NVIDIA's strategic shift from a GPU hardware leader toward building an open large‑model ecosystem, providing practical, high‑performance infrastructure for the multi‑agent era to the global AI community.
Nemotron 3 Super is the second model in the Nemotron 3 family (following Nano, released in December 2025), designed specifically for "high‑accuracy, multi‑agent collaboration" scenarios. NVIDIA's research team emphasizes in the technical report that the model achieves up to a 5x inference throughput improvement while maintaining high accuracy, making it particularly suitable for software development, IT operations, cybersecurity triage, and complex workflow automation that require multiple agents to collaborate. The model's Hugging Face downloads surpassed several hundred thousand within 24 hours of release, quickly topping open‑model leaderboards.
Core features: hybrid architecture and efficient design
The standout feature of Nemotron 3 Super is its hybrid architecture (Hybrid Mamba‑Transformer‑MoE), NVIDIA's first deep fusion of three leading techniques:
- LatentMoE (a new expert‑mixture mechanism): Unlike traditional MoE (with fixed activated parameters), LatentMoE uses dynamic expert routing and implicit representations to achieve a balance of "high accuracy + low active parameters." The model contains 120B total parameters, but only 12B are activated per inference, significantly reducing memory footprint and compute cost. The technical report shows this design yields over a 15% accuracy improvement versus traditional MoE at the same FLOP.
- Mamba‑2 + Transformer hybrid: By combining Mamba's state‑space models (efficient long‑sequence handling) with Transformer attention mechanisms, the design addresses the "context explosion" issue that affects traditional models on long contexts. The model natively supports a 1M‑token context window, far exceeding most open models and enabling easy handling of very long documents, codebases, or extended multi‑turn Agent dialog histories.
- Multi‑Token Prediction (MTP) layer: A new multi‑token prediction mechanism that supports native speculative decoding to further accelerate inference. NVIDIA's benchmarks show that in an 8K input / 64K output scenario, Nemotron 3 Super's throughput reaches 2.2x that of GPT‑OSS‑120B and 7.5x that of Qwen3.5‑122B.
Additionally, the model was pretrained using NVFP4 low‑precision training, achieving stable convergence during training and substantially lowering training cost. NVIDIA also open‑sourced the full training dataset (25 trillion tokens, staged in two phases) and the recipes, allowing developers to reproduce or fine‑tune the model on their own clusters.
Performance: rivaling GPT‑5.4, leading on Agentic tasks
NVIDIA and third‑party benchmarks (Artificial Analysis, PinchBench, etc.) indicate Nemotron 3 Super excels on Agentic AI metrics:
- General intelligence benchmarks: Scores on multilingual tasks such as MMLU‑ProX and WMT24 approach GPT‑5.4, with some Agentic scenarios (e.g., tool invocation, long conversations) matching or slightly exceeding it.
- Agentic‑specific benchmarks: On PinchBench (Agent frameworks like OpenClaw), the model scored 85.6%, the highest among open models.
- Inference speed and efficiency: Output speed reaches up to 478 tokens/s (Artificial Analysis), far exceeding comparable open models. Latency and throughput advantages are especially pronounced in multi‑agent collaboration scenarios.
- Speech and long‑context: In long‑dialogue speech‑Agent benchmarks, performance is comparable to GPT‑5.4 and GPT‑4.1, making it suitable for real‑time customer service, meeting summarization, and similar applications.
NVIDIA emphasizes Nemotron 3 Super is focused not on "maximum parameters" but on "optimal efficiency." It performs strongly in enterprise multi‑agent systems such as IT ticket automation, cybersecurity triage, and complex software development workflows.
Open‑source strategy and ecosystem impact
Unlike many closed models, Nemotron 3 Super is released under a fully open license (NVIDIA Open Model License): weights, datasets, training code, and inference optimizations are all open‑sourced. Developers can deploy immediately via Hugging Face, NVIDIA NIM, Together AI, and other platforms, with commercial use supported. This continues NVIDIA's Nemotron‑series approach aiming to build an open, efficient Agentic AI ecosystem.
The industry impact is clear:
- Lowering the barrier for enterprises: Small and medium‑sized companies can deploy high‑performance Agent systems without buying costly closed‑source APIs.
- Accelerating multi‑agent applications: Provides a powerful open‑source brain for frameworks like OpenClaw and CrewAI, speeding Agentic AI from concept to large‑scale deployment.
- Opportunities for Chinese developers: Domestic models (e.g., Qwen, DeepSeek) can reference this hybrid architecture to further improve efficiency and open‑source competitiveness.
Challenges and outlook
Despite its advantages, Nemotron 3 Super has limitations: its 120B size still poses challenges for consumer‑grade hardware deployment; some very hard reasoning tasks remain behind the top closed GPT‑5.4 variant; and although the open license is permissive, commercial deployments must pay attention to compliance details.
NVIDIA plans to release Nemotron 3 Ultra (a larger‑scale version) in the second half of 2026 and will continue optimizing the inference engine. Combined with hardware support expected at GTC, the Nemotron 3 family could become the open benchmark for the Agentic AI era.
Conclusion: an accelerator for open Agentic AI
The release of NVIDIA Nemotron 3 Super, with its 120B hybrid architecture and efficient Agentic capabilities, rivals cutting‑edge closed models like GPT‑5.4. It not only provides developers with a powerful tool but also promotes the industry's shift toward efficient, multi‑agent systems through a fully open approach.
In 2026, as AI transitions from single models to collaborative agents, Nemotron 3 Super marks a deep convergence of open‑source communities and large‑vendor hardware advantages. Whoever masters efficient Agentic infrastructure will gain an edge in AI application deployment, and NVIDIA's move may be another important milestone in the new era of open large models.
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