LLMAug 12, 2026, 6:59 AM

NVIDIA AI Releases Nemotron 3.5 Lightning: A 30B Open MoE with 3B Active Parameters, and NeMo Switchyard Model Router

30-second summary

NVIDIA has released Nemotron 3.5 Lightning, an open 30 billion parameter Mixture-of-Experts (MoE) model with 3 billion active parameters. It is designed for agent execution and utilizes NeMo Switchyard for efficient model routing.

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NVIDIA AI Releases Nemotron 3.5 Lightning: A 30B Open MoE with 3B Active Parameters, and NeMo Switchyard Model Router
Key takeaways
  • NVIDIA released Nemotron 3.5 Lightning, a 30B open MoE model with 3B active parameters.
  • The model is optimized for AI agent execution.
  • NeMo Switchyard technology enables efficient routing to the cheapest capable model for each step.
  • This release contributes to NVIDIA's open-source AI ecosystem.
Full story

NVIDIA has introduced Nemotron 3.5 Lightning, a new open-source Mixture-of-Experts (MoE) model. This model boasts 30 billion total parameters but activates only 3 billion per inference, making it more efficient for specific tasks.

It is specifically targeted at the agent execution layer, aiming to enhance the capabilities of AI agents. A key component of this release is NeMo Switchyard, a model router that intelligently directs each step of an agent's process to the most cost-effective and capable model within the MoE architecture.

This development signifies NVIDIA's continued push into open-source AI development, providing tools and models that can be integrated into various AI agent frameworks and applications.

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Why this matters
Developers

Provides a new open-source MoE model and routing technology for building efficient AI agents.

Businesses

Offers potential for more cost-effective AI agent deployment and execution.

Investors

Highlights NVIDIA's ongoing innovation and investment in the AI infrastructure and open-source space.

Everyone

Advances the efficiency and capability of AI agents through open-source innovation.

Glossary
Mixture-of-Experts (MoE)
An AI model architecture where multiple smaller 'expert' networks are combined, and only a subset is activated for any given input, improving efficiency.
Agent execution layer
The part of an AI system responsible for carrying out tasks or actions based on decisions made by the AI.
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