New NVIDIA Nemotron 3 Super Delivers 5x Higher Throughput...

What’s Happening

Not gonna lie, Shipped today, NVIDIA Nemotron 3 Super is a 120‑billion‑parameter open model with 12 billion active parameters designed to run complex agentic AI systems at grow.

Available now, the model combines advanced reasoning capabilities to efficiently complete tasks with high accuracy for autonomous agents. AI-Native companies: Perplexity offers its users access to Nemotron 3 Super for [] New NVIDIA Nemotron 3 Super Delivers 5x Higher Throughput for Agentic AI A new, open, 120-billion-parameter hybrid mixture-of-the experts model optimized for NVIDIA Blackwell addresses the costs of long thinking and context explosion that slow autonomous agent workflows. (and honestly, same)

By Kari Briski This Article X Facebook LinkedIn Copy link Link copied!

The Details

Companies offering software development agents like CodeRabbit , Factory and Greptile are integrating the model into their AI agents along with proprietary models to achieve higher accuracy at lower cost. And life sciences and frontier AI organizations like Edison Scientific and Lila Sciences will power their agents for deep literature search, data science and molecular understanding.

Enterprise software Platforms: Industry leaders such as Amdocs , Palantir , Cadence , Dassault Systèmes and Siemens are deploying and customizing the model to automate workflows in telecom, cybersecurity, semiconductor design and manufacturing. As companies move beyond chatbots and into multi‑agent applications, they encounter two constraints.

Why This Matters

The first is context explosion. Multi‑agent workflows generate up to 15x more tokens than standard chat because each interaction requires resending full histories, including tool outputs and intermediate reasoning. Over long tasks, this volume of context increases costs and can lead to goal drift, where agents lose alignment with the original objective.

This adds to the ongoing AI race that’s captivating the tech world.

The Bottom Line

Over long tasks, this volume of context increases costs and can lead to goal drift, where agents lose alignment with the original objective. The second is the thinking tax.

Is this a W or an L? You decide.