NVIDIA Expands Nemotron Family with High-Efficiency Models for Specialized Agents

NVIDIA has released the latest addition to its Nemotron family, a range of high-efficiency models designed specifically for specialized agents. The new models are part of the Nemotron 3.5 series and include Super VL, Bolt 74B-A9B 24GB, and Bolt E4B 4GB.

The Nemotron 3.5 Super VL model is an open 120-billion-parameter model that delivers exceptional performance in task completion and accuracy for specialized always-on agents. It has been optimized to handle high-volume tasks across workflows spanning days or weeks.

Super VL is designed to be highly efficient, with a parameter count roughly one-fifth the size of NVIDIA’s largest reasoning model while maintaining leading accuracy on multimodal agentic tasks such as instruction following, tool use, and visual understanding.

The Nemotron 3.5 Bolt models are optimized for edge computing and provide a balance between intelligence and speed per GB. The two new edge models join Lightning and Ultra in the Nemotron family, each designed to handle specific types of agent workloads.

Bolt 74B-A9B 24GB is available now and has been optimized for local agentic tasks on NVIDIA RTX/DGX Spark systems using native 2-bit quantization. It provides a good balance between intelligence and speed per GB, making it suitable for everyday agentic tasks such as researching the web or writing code.

Bolt E4B 4GB is designed to run physical and conversational agents on GeForce RTX, Jetson, and RTX/DGX Spark systems. It combines natural conversation with multi-turn agentic capabilities to power fast local voice assistants and interactive characters.

The Nemotron family now covers the full range of agent deployments, from large enterprise servers down to edge devices and local hardware. Any organization can choose an open model optimized for their specific tier without having to compromise between capability and deployability.

Super VL has been built in collaboration with the Nemotron Coalition and delivers leading accuracy on multimodal agentic tasks comparable to NVIDIA’s largest reasoning model at roughly one-fifth the parameter count. It is also highly efficient, helping enterprise agents complete high-volume tasks faster across workflows spanning days or weeks.

The Bolt models are designed for edge computing and provide a balance between intelligence and speed per GB. They have been optimized for local agentic tasks on NVIDIA RTX/DGX Spark systems using native 2-bit quantization, making them suitable for everyday agentic tasks such as researching the web or writing code.

Super VL’s vision language strengths are targeted at what agents encounter in practice: optical character recognition, tables, charts, and structured information in visual documents. It processes a web page, extracts text, reads graphs and charts, and returns insights from what it finds.

The same applies to PDFs and other documents that agents commonly work with. Super VL understands images, webpages, and PDFs and acts on its understanding, combining text and visual understanding in one model means agents don’t need to route tasks between separate systems – context carries through as work moves from document to data to decision.

Openness and customizability reinforce each other. The Nemotron 3.5 Super VL model ships with open weights, open datasets, and training recipes. Control runs in two directions: organizations can run the model wherever their security and data-residency requirements dictate, or they choose to adopt a model they can adapt rather than one subject to a provider’s restrictions.

Customization is supported through NVIDIA NeMo, Python-based pipelines, and open datasets. Organizations are post-training Super VL on their own data, tools, policies, and workflows to push accuracy higher on specialized tasks that define their agent deployments.

The Nemotron 3.5 Super VL model was trained on about 26 trillion tokens across pretraining and continued pretraining, then post-trained with roughly 3.8 million samples and reinforcement learning across dozens of environments. NVIDIA is also releasing open datasets on Hugging Face spanning agentic tool use, finance, instruction following, long context, fairness, and vision-language understanding.

NVIDIA NIM packages the model as a production-ready microservice with performance-optimized, validated configurations, and enterprise-grade support refreshed every two weeks with security patches and performance improvements. With the model running on NVIDIA Dynamo, organizations can serve up to 8x more users with 85% lower latency.

The Nemotron 3.5 Super VL model is available now on Hugging Face, Open Router, ModelScope community, and as NVIDIA NIM. It runs across NVIDIA’s full hardware stack: DGX Spark, DGX Station, RTX PRO, and RTX systems for local intelligence at the desktop, on NVIDIA Jetson at the edge, and across data center and cloud environments.

The Nemotron 3.5 Bolt models are also available now, with Super VL being post-trained by dozens of organizations across industries to push accuracy higher on specialized tasks that define their agent deployments.