Nvidia's Switchyard router reshuffles AI models mid-task, cutting task costs to a third in its own tests

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Nvidia's Switchyard router reshuffles AI models mid-task, cutting task costs to a third in its own tests

AI llminfrastructure VentureBeat

Nvidia's new Switchyard router dynamically reroutes agent workflows to the most cost-efficient AI model mid-task, reportedly cutting expenses by two-thirds.

What: Nvidia introduced the Nemotron 3.5 Lightning (30B parameters) alongside NeMo Switchyard, an open-source orchestration library. Lightning claims 4x faster output speed than competitors and can complete agent tasks 30% faster than Qwen3.6-35B.

Why it matters: As agentic workflows move from simple chat to multi-step execution, cost management through 'model switching'—using smaller, faster models for simple tasks and larger, more capable ones for reasoning—is becoming a critical industry standard.

Deep dive

Nemotron 3.5 Lightning: A 30B MoE model optimized for low-latency specialized agent tasks.
NeMo Switchyard: Orchestration layer that evaluates task difficulty in real-time to select the optimal model.
Performance: Claims 30% faster execution speed over Qwen3.6-35B.
Cost Efficiency: Enables frontier-level completion at approximately 33% of the cost of running OpenAI's Opus 4.8.

Decoder

Mixture-of-Experts (MoE): An architecture where a model consists of several smaller sub-networks (experts), with only a fraction of the parameters activated per token to reduce compute costs while maintaining performance.
Agentic workflow: A multi-step autonomous process where an AI system breaks down a high-level goal into sub-tasks, executes them, and manages state.

Original article

Nvidia has announced Nemotron 3.5 Lightning, a 30-billion-parameter open mixture-of-experts model built for high-volume, specialized agent tasks, and NeMo Switchyard, an open-source library that routes each step of an agent workflow to whichever model fits it best. Lightning delivers up to 4x faster output than comparable models in its class. It can complete agentic tasks roughly 30% faster than Qwen3.6-35B at matching accuracy. Paired with Switchyard, the model retains frontier-level task completion ability while costing roughly a third of running Opus 4.8 alone.

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