NVIDIA releases Nemotron AI models for enterprise agents
NVIDIA Blog: Open From Data Center to Desktop: NVIDIA Expands Nemotron Family With Highest Efficiency Super VL and Bolt Models
NVIDIA today released Nemotron 3.5 Super VL, an open 120-billion-parameter model that delivers the fastest task completion and highest accuracy for specialized always-on agents. Joining it are two new edge models: Nemotron 3.5 Bolt 74B-A9B 24GB, optimized to balance intelligence and speed per GB for local agentic workloads, and Nemotron 3.5 Bolt E4B 4GB, optimized for intelligence per GB for fast conversational agents. Super VL and Bolt 74B-A9B are available now, with Bolt E4B coming Oct. 23. T
What happened
NVIDIA released Nemotron 3.5 Super VL, an open 120-billion-parameter multimodal model designed to run enterprise AI agents that process text, images, documents, and videos continuously. The company also released two smaller Bolt models optimized for edge devices and local machines. The models use a Mixture-of-Experts architecture where only a fraction of parameters are active per task, allowing faster and more efficient performance.
What each outlet adds
- NVIDIA Blog ↗: The Super VL model delivers the fastest task completion and highest accuracy for specialized always-on agents, while the Bolt models enable tasks like research, writing, and coding to run locally without sending data to the cloud.
- The Decoder ↗: Dozens of organizations are already fine-tuning Super VL, and NVIDIA is optimizing everything for its own hardware to drive chip and system sales.
Why it matters
The models give organizations choices in where and how to deploy AI agents based on their security, privacy, and performance needs. NVIDIA is publishing the models alongside datasets and training recipes to support enterprise adoption.
Get smart on it
NVIDIA released a family of open AI models called Nemotron designed to run agents, which are AI systems that can complete tasks autonomously, across different scales of hardware from large data centers down to personal computers. The largest model, Super VL, is optimized for accuracy and speed on specialized business tasks while remaining customizable by organizations on their own data and infrastructure. Smaller Bolt models bring agent capabilities to edge devices with limited computing power, allowing tasks like research, writing, and coding to run locally without sending data to the cloud. These models matter because they give organizations choices in where and how to deploy AI agents based on their security, privacy, and performance needs rather than being locked into a single provider's restrictions.
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