Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning model for every execution step adds cost and latency.
NVIDIA Nemotron 3.5 Lightning is an open 30B mixture-of-experts (MoE) model with 3B active parameters built for that execution layer of always-on agents. It is designed for harnesses like OpenClaw and Hermes Agent—all supported by the NVIDIA NemoClaw open source security and management stack for running always-on AI agents.
The NVIDIA Nemotron open model family is like a software library, with each release continuously improving accuracy and speed. As these models evolve, the rapid maturation of model routing and orchestration is also underway.
This is important because developers increasingly build applications with a system of models. Frontier reasoning models such as Nemotron 3 Ultra handle orchestration and complex planning while smaller, more efficient models handle the high-volume execution layer.
This post introduces NVIDIA Nemotron 3.5 Lightning and explains how its smaller MoE design is optimized for high-volume, low-latency execution in autonomous agents. It also details the inference and training innovations that power it. Finally, the post also introduces NVIDIA NeMo Switchyard, a library that intelligently routes each task to the best model for the job.
