NVIDIA open-sources Nemotron 3 Super: 120B MoE, 12B active, 1M context, built for agents
NVIDIA released Nemotron 3 Super, a 120B-total / 12B-active hybrid Mamba-Transformer mixture-of-experts model aimed at multi-agent workloads, with fully open weights, datasets and training recipes.
It is the first Nemotron 3 model pretrained natively in NVFP4, introduces a new LatentMoE routing architecture, and adds multi-token-prediction layers for native speculative decoding — together yielding over 5x the throughput of the previous Nemotron Super, and 2.2x and 7.5x higher inference throughput than GPT-OSS-120B and Qwen3.5-122B respectively. Native context is 1M tokens. On PinchBench, a benchmark for an LLM acting as the brain of an OpenClaw agent, NVIDIA reports 85.6%.
WHY IT MATTERS
An open-weight model explicitly engineered around the two economics that kill agent stacks — context re-sending and the per-step reasoning tax — with the full pretraining recipe published.

