NVIDIA’s Nemotron 3.5 Lightning and NeMo Switchyard solve a concrete pain point for teams building long‑running AI agents: every step—tool calls, result validation, sub‑agent delegation—gets sent to a large frontier model, driving up cost and latency while offering little extra value for routine work.
Lightning is a 30‑billion‑parameter mixture‑of‑experts model that activates only about 3 billion parameters per token. Its hybrid Mamba‑2 + MoE + Attention design, speculative decoding (with DSpark and DFlash draft models), and NVFP4 low‑precision quantization let it run up to four times faster than similar‑sized dense models. On the PinchBench benchmark it reaches 86 % accuracy while finishing 10 000 tasks 30 % quicker than Qwen3.6 35B at comparable accuracy. The model is released under the permissive OpenMDW‑1.1 license, with open weights, training data and recipes, and is cleared for commercial use. It fits on a single modern GPU—either a DGX Spark (GB10) or an H100—making it accessible to solo developers, seed‑stage startups, mid‑market teams via providers like Baseten, Together AI or Nebius, and to regulated enterprises that want to keep everything on‑premises.
NeMo Switchyard is an open‑source routing library that directs each agent step to the most efficient model available. Its tunable routers—LLM classifier, stage router, escalation router, and prefill router—let a workflow start with Lightning and only invoke a frontier model when sustained difficulty is detected. In LangChain tests, routing between Lightning and Claude Opus 4.8 cut cost by 74 % versus a frontier‑only baseline, sending just 7 % of calls to the large model while incurring only a ~6‑point accuracy drop. Similar gains were shown in Cognition’s Devin Desktop implementation.
Together, Lightning handles the high‑volume execution layer, and Switchyard ensures the right model is used at the right time, delivering lower latency, reduced spend, and broader accessibility for agents in cybersecurity, legal, software engineering, finance, healthcare and life‑sciences workflows.
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