Nvidia launches new open models to run agents in the enterprise and on PCs

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. 

While such claims are standard whenever a major tech company announces a new model, Nvidia has backed it up with a list of heavy-hitter companies that it says are using Super VL. These include Skydance, EY, Ramp, Crowdstrike, Perplexity, and Salesforce.  

Of course, these aren’t the first open-weight models that Nvidia has released that compete with the proprietary frontier labs — companies that are both its customers and its investments. The GPU giant has an entire family of Nemotron models that do everything from multimodal artificial intelligence to reasoning, coding, math, and enterprise automation. 

However, those fast-growing proprietary AI labs are now looking to many other chips and AI system makers beyond Nvidia to power their needs, like AWS’s Trainium and Cerebras, as well as building their own home-grown chips.  

So Nvidia is leaning hard into the trend of enterprise companies wanting to run their own open models in their own data centers. “Unlike closed frontier models, organizations can post-train Super VL on their own data, deploy it on their own infrastructure and own what they build,” Briski wrote. 

But with the announcement or the smaller, local PC models, it is also trying to push forward the nascent trend of consumer AI assistants. While it seems a new AI assistant is introduced every day by startups and Big Tech alike, privacy-weary consumers may not want to invite such agents so deeply into their lives giving them access to info about their families, health, finances, and so on.  

So, while Nvidia hopes to create a new market for itself selling PCs powered by its chips by creating machines that run the models and agents locally, PC users “can delegate everyday projects … without sending those workloads to the cloud,” Briski wrote. 

Of the two Bolt models, Nemotron 3.5 Bolt 74B-A9B 24GB is a 74-billion parameter model designed for local agentic tasks. It is intended to balance speed with reasoning to handle multistep reasoning, tool calling, and web browsing. The smallest Bolt E4B is aimed at conversational agents. 

Super VL and Bolt 74B-A9B are available now, with Bolt E4B coming later this month, Nvidia says.