Nvidia Expands Open Model Portfolio with Nemotron 3.5 Super VL and Bolt Series

Nvidia releases new open weight models for enterprise agents and local AI PCs, with claims of faster task completion than major competitors.

Nvidia has announced the launch of new open weight models aimed at always on agents and edge computing, signaling a significant push into both enterprise and consumer AI markets. The announcement, made on Thursday, October 19, 2026, introduces the Nemotron 3.5 Super VL for enterprise use and two lightweight Bolt models designed for local execution on AI PCs and workstations.

Enterprise Focused Nemotron 3.5 Super VL

The centerpiece of the announcement is the Nemotron 3.5 Super VL, a 120 billion parameter model specifically designed for enterprise agent deployments. While it operates with fewer parameters than Nvidia’s Nemotron 3 Ultra model, the company claims it delivers comparable accuracy in instruction following and tool use tasks.

Nvidia asserts that Super VL completes tasks faster than competing models from Anthropic and OpenAI, including Opus 5, GPT 6.1 Sol, Luna, and Astra, when post trained on specific agentic tasks. These tasks encompass document intelligence, financial accounting, scientific experimentation, medical insurance claims, and security attack resilience.

The company has backed these performance claims with a roster of notable organizations reportedly using the model, including Skydance, EY, Ramp, Crowdstrike, Perplexity, and Salesforce.

Lightweight Bolt Models for Local AI PCs

At the opposite end of the spectrum, Nvidia introduced two compact models in its Bolt lineup:

Nemotron 3.5 Bolt 74B A9B 24GB: A 74 billion parameter model designed for local agentic tasks, balancing speed with reasoning capabilities for multistep reasoning, tool calling, and web browsing.

Nemotron 3.5 Bolt E4B: The smallest model in the lineup, aimed at conversational agents.

These models are intended to run locally on the new generation of AI PCs featuring Nvidia’s RTX Spark chip, as well as workstations running the DGX Spark. Such systems, targeting consumers, programmers, and enterprise knowledge workers, are being released to market through partners including Microsoft and Dell.

Strategic Positioning in the AI Landscape

The announcement reflects Nvidia’s strategic response to evolving market dynamics. While the company maintains an extensive Nemotron family covering multimodal AI, reasoning, coding, math, and enterprise automation, major proprietary AI labs, which are both Nvidia’s customers and investments, are increasingly looking to alternative chips and systems from AWS’s Trainium, Cerebras, and homegrown silicon solutions.

Nvidia is therefore leaning into the growing trend of enterprises wanting to run open models within their own data centers. According to Kari Briski, Nvidia’s vice president of Open Models and Software, “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.”

The company also aims to advance the emerging consumer AI assistant market by creating machines that run models and agents locally. This addresses privacy concerns among consumers who may be reluctant to grant cloud based agents access to sensitive personal information regarding their families, health, and finances. As Briski notes, PC users can “delegate everyday projects … without sending those workloads to the cloud.”

Availability

The Nemotron 3.5 Super VL and Bolt 74B A9B models are available now, while the Bolt E4B is scheduled for release later this month.

Conclusion

Nvidia’s latest open model releases represent a calculated dual strategy. On the enterprise side, the Nemotron 3.5 Super VL offers organizations a powerful, customizable alternative to closed frontier models, allowing them to maintain full ownership and control over their AI deployments. On the consumer side, the Bolt series positions Nvidia at the forefront of the emerging local AI PC market, addressing growing privacy concerns while creating new opportunities for its chip technology. By providing optimized models across the deployment spectrum, from data centers to personal computers, Nvidia is working to ensure it remains indispensable to the AI ecosystem, even as its largest customers explore alternative hardware solutions.

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