Nvidia launches Nemotron 3.5 Super VL and Bolt open-weight models for agents
Nvidia launched a family of open-weight models aimed at always-on enterprise agents and edge computing, led by Nemotron 3.5 Super VL, a 120-billion-parameter model for enterprise agents. Nvidia says it delivers accuracy similar to the larger Nemotron 3 Ultra on instruction-following and tool use, and claims it completes certain agentic tasks faster than Anthropic and OpenAI models after post-training. Two lightweight Bolt models target local agent work on RTX Spark AI PCs and DGX Spark workstations, with Super VL and Bolt 74B-A9B available now and Bolt E4B due later this month.
Nvidia is pitching open-weight models that organizations can post-train and deploy on their own infrastructure, alongside local PC models intended to keep agent workloads off the cloud.
Nvidia on Thursday launched new open-weight models designed for always-on agents and edge computing.
The Nemotron 3.5 Super VL is a large, 120-billion-parameter model geared for agents used by the enterprise. While it uses fewer parameters than the Nemotron 3 Ultra model, Nvidia says it can deliver similar accuracy in instruction-following and tool-use tasks.
At the other end of the spectrum, the company also launched two lightweight Bolt models. These are intended to run locally on the new AI PCs (running the Nvidia RTX Spark chip) and workstations running the DGX Spark. Such PCs, aimed at consumers, programmers, and enterprise knowledge workers, are being released right now into the market through partners like Microsoft and Dell.
“Any organization that wants to run agents on Nvidia infrastructure now has an open, optimized model for their tier without having to choose between capability and deployability,” Nvidia’s vice president of Open Models and Software, Kari Briski, wrote in the blog post announcing these models.
Nvidia claims that the Super VL completes tasks faster than competing models from Anthropic and OpenAI (Opus 5, GPT 6.1 Sol, Luna, Astra) when post-trained on specific agentic tasks. These tasks include document intelligence, financial accounting, scientific experimentation, medical insurance claims, and security attack resilience.
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