Introducing Nemotron 3 Super

NVIDIA Nemotron 3 Super for agentic AI in financial services Announcing our Series F . Learn more

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NVIDIA Nemotron 3 Super for agentic AI in financial services

Day-zero support for NVIDIA Nemotron 3 Super, a 120B hybrid MoE model with 12B active params, 1M token context, and over 50% faster token generation.

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Rachel Rapp

Last updated March 11, 2026

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TL;DR NVIDIA Nemotron 3 Super is a 120B hybrid mixture-of-experts model with 12B active parameters per forward pass. It delivers over 50% faster token generation than comparable models in the landscape and leads benchmarks across AIME 2025, SWE-Bench Verified, terminal-bench, and RULER. Nemotron 3 Super balances accuracy and efficiency for multi-agent workloads and is completely open-source, making it particularly useful for sensitive, complex financial service applications. We’re excited to be a launch partner with day-0 support for NVIDIA Nemotron 3 Super, powered by the Baseten Inference Stack . You can try it here .

Nemotron 3 Super and the case for open agentic AI As GenAI applications move towards multi-agent systems—retrievers, planners, tool executors, and verifiers working in concert—their infrastructure demands change. Communication overhead, context drift, and inference calls scale with agent count. Open models give you more control over these demands: customize behavior for domain-specific tasks, tune your agentic system for orchestration efficiency, and deploy on single-tenant or self-hosted infrastructure to meet compliance requirements. For applications like financial services running sensitive workloads, these are often hard requirements. That’s what Nemotron 3 Super is built for: open weights, open training data, and open recipes for full control. ✕ Nemotron 3 Super is the only model to fully land in the most attractive quadrant for openness vs. intelligence according to Artificial Analysis, in contrast to models in the Qwen, GLM, and MiniMax families. Nemotron 3 Super quality and performance Open models need to be high-quality — and fast — to be useful. In terms of quality, Nemotron 3 Super achieves leading results across relevant benchmarks for agentic reasoning tasks, including: AIME 2025: leading math reasoning accuracy

terminal-bench : agentic coding and command execution

SWE-Bench Verified and Multilingual : software engineering task completion

RULER : long-context retrieval and reasoning across extended inputs

At the same time, Nemotron 3 Super generates tokens over 50% faster than comparable models, driven by its hybrid Mamba-Transformer architecture and multi-token prediction (more on both below). For multi-agent systems running dozens of concurrent agents, this difference can compound in terms of speed (user experience) and compute costs. ✕ Nemotron 3 Super achieves higher tokens per second (TPS) than comparable models in the Qwen, MiniMax, GLM, and gpt-oss families, according to Artificial Analysis. It’s the only model to fully land in the most attractive quadrant for intelligence vs. efficiency. Nemotron 3 Super architecture and model design Nemotron 3 Super uses a hybrid Mamba-Transformer architecture combined with Mixture-of-experts routing. A few design choices are worth understanding in detail. Latent MoE architecture In a standard mixture-of-experts (MoE) model, each token is routed to a small subset of experts (i.e., specialized subnetworks) that process it and return a result. The tradeoff is that routing tokens between experts in full-dimensional token space is expensive: you're moving large vectors around, which adds memory and communication overhead. Nemotron 3 Super’s latent MoE architecture adds a step to decrease this overhead. Before routing, each token is projected to a smaller latent representation for expert routing and computation, then back to the full token space. Because the routing and computation happen in a lower dimension, you can afford to consult more experts per token without paying the usual cost. That’s how Nemotron 3 Super consults 4 experts at the cost of 1. Multi-token prediction (MTP) Multi-token prediction (MTP) predicts several tokens simultaneously in a single forward pass. Ablation studies from NVIDIA's Nemotron paper show ~97% acceptance on the first two predicted tokens, meaning the speculation almost always holds. This accelerates long-form generation significantly — particularly relevant for financial workflows generating structured outputs like loan summaries, risk assessments, or audit reports. NVFP4 training NVFP4 training uses NVIDIA's 4-bit floating point format, trained on Blackwell architecture. Peak FP4 throughput on GB300 is 3x higher than FP8. Sensitive layers (like Mamba output projections and MTP projections) are kept at higher precision (BF16 or MXFP8) to prevent information loss. This gives you the speed of 4-bit quantization without meaningful accuracy degradation. Context length 1M token context length lets agents retain full conversation histories, maintain plan state across multi-step workflows, and feed entire document sets into a single RAG pipeline call. Cross-document reasoning (like comparing loan applications, regulatory filings, or transaction records across long time horizons) becomes tractable. Standard transformer attention scales KV cache linearly with sequence length — at 1M tokens, that becomes a memory problem. Mamba layers avoid this entirely by compressing sequence state into a fixed-size representation, keeping memory overhead constant regardless of context length. When to use which Nemotron 3 model Nemotron 3 Super is one of three Nemotron 3 models, each a different size and targeting a different point on the accuracy-efficiency curve. Nemotron 3 Nano Parameters: 30B total, 3B active Nano is the most compute-efficient model in the family. With 3B active parameters, it delivers 4x the throughput of Nemotron 2 Nano and is optimized for high-volume, targeted tasks: summarization, retrieval, classification, and routing. In a multi-agent pipeline, Nano works well as the high-frequency worker — handling intermediate steps without becoming a bottleneck. Use Nano when you need to run many agents at scale, and the task doesn't require deep reasoning. Nemotron 3 Super Parameters: 120B total, 12B active Super is the right choice for agents that need to combine multiple inputs, reason across steps, and call tools reliably. It's explicitly optimized to run many collaborating agents...