NVIDIA Nemotron 3.5 Lightning and NeMo Switchyard Deliver Faster, Smarter, More Efficient Agentic AI
- Source
- blogs.nvidia.com
- Author
- Kari Briski
- Date
Context
NVIDIA's Nemotron 3.5 Lightning release is aimed at specialized work inside larger systems. The company describes a division of labor in which a larger model plans a workflow while a smaller, customizable model handles targeted tasks such as code review or billing questions. Lightning can be adapted using an organization's own data, tools and workflows. The announcement sets out that intended role.
The accompanying NeMo Switchyard library supplies the selection mechanism: developers configure routing policies around quality, latency and cost. Together, the releases provide a model to specialize and software to decide when to use it, with the resulting tradeoff depending on the workload.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- mixture-of-experts — A model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.
- multi-agent — Using several AI agents on one problem — splitting work in parallel, checking each other, or filling different roles like planner and reviewer.
“The model delivers up to 4x faster output speed, leading to 30% faster agentic task completion compared with other models in its class.”
Kari Briski
“Internal benchmarks show that NeMo Switchyard maintains frontier-level accuracy while reducing task completion cost to nearly one-third of Opus 4.8 alone.”
Kari Briski
“If customers rely on one default model, they might either overspend or lose quality; if they manage routing manually, it becomes integration work that can slow down a deployment.”
Kari Briski
“With NeMo Switchyard, achieved 74% lower cost in 145 multi-turn Deep Agents tasks by routing only 7% of calls to a frontier model, at a 6% accuracy tradeoff.”
Kari Briski
“Used NeMo Switchyard to match a frontier model’s performance while cutting costs by 58% and runtime by 33% in Ramp SWE-Bench”
Kari Briski
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