Nvidia expands Nemotron LLM family with hyper-efficient models for agentic systems

Nvidia Corp. launched the Nemotron 3.5 Super VL, a 120-billion-parameter open multimodal large language model, alongside the edge-focused Nemotron 3.5 Bolt models for agentic systems. Nvidia said Super VL matches its largest reasoning models at one-fifth the size and produces outputs nearly five times faster than other models in its class, trained on about 26 trillion tokens, 3.8 million post-training samples and reinforcement learning across 100 open environments. The Bolt 74B-A9B 24GB model activates 9 billion of its 74 billion parameters and uses 2-bit quantization to fit in 24GB of memory, while the smaller Bolt E4B 4GB model arrives later this month for GeForce RTX, Jetson and RTX/GTX Spark edge devices.

Nvidia expands Nemotron LLM family with hyper-efficient models for agentic systems Nvidia Corp . today announced https://blogs.nvidia.com/blog/nemotron-3-5-super-vl/ the launch of customizable Nemotron 3.5 Super LV large language models for always-on artificial intelligence agents and edge-focused 3.5 Bolt for local systems. The company said the three new models will cover the full range of agent deployments, from large enterprise servers to edge devices and local hardware. Nemotron 3.5 Super VL is an open 120-billion-parameter model, built in collaboration with the Nemotron Coalition https://nvidianews.nvidia.com/news/nvidia-launches-nemotron-coalition-of-leading-global-ai-labs-to-advance-open-frontier-models , a group of model builders and AI labs advancing open frontier-level models, designed for high-accuracy multimodal agentic tasks. It is capable of instruction following, tool use, multistep business workflows and visual understanding. Nvidia said it has comparable power to the company’s largest reasoning models at one-fifth the size. That size, plus a new architecture, lets Super VL produce outputs nearly five times faster than other models in the same class. Unlike closed frontier models, Nvidia provides the Super VL weights in the open, meaning organizations can post-train it on their own data and deploy it on their own infrastructure. This means they can bring it behind their firewall and keep it inside their domain, maintaining sovereignty, privacy and safety. To build Super VL, Nvidia trained the model with open data and open methods using about 26 trillion tokens across pretraining and continued pretraining. This also included 3.8 million post-training samples and reinforcement learning across 100 open environments to fine-tune the model. Nvidia also said it’s releasing open datasets on Hugging Face spanning agentic tool use, finance, instruction following, long context, safety, fairness and vision-language understanding. Bolt 74B-A9B 24GB is a smaller model designed to run agentic tasks locally and is based on Super VL. It uses a hybrid mixture-of-experts model that activates 9 billion of its 74 billion parameters at a time and uses 2-bit quantization to fit within 24 gigabytes of memory. This lets it run comfortably on an enterprise workstation or PC graphics processing units, so users can delegate everyday projects, including writing and debugging code, without sending workloads to the cloud. Later this month, Nvidia is also launching an even smaller model: Bolt E4B 4GB. This tiny model is built for edge deployment on GeForce RTX, Jetson and RTX/GTX Spark. 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