Build Visual AI Agents With NVIDIA Nemotron 3.5 Super VL
Summary
NVIDIA Nemotron 3.5 Super VL is an open-source, multimodal LLM designed for efficient agent deployment, featuring 120 billion total parameters and 12 billion active parameters. The model combines multimodal reasoning, tool use, and code generation, allowing it to process text, images, charts, documents, and video. A demonstration showcases an AI agent using Nemotron 3.5 Super VL to investigate a synthetic billing issue, successfully identifying a 30% cost increase and generating a Python-based proposal to optimize ticket routing, projecting $100,000 in monthly savings.
Key takeaways
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Model Architecture and Efficiency
Nemotron 3.5 Super VL is an Ultra-class model, distilling the capabilities of the Nemotron 3 family. It maintains high performance while fitting on a single NVIDIA DGX Spark, making it efficient for agent deployment. (00:00)
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Multimodal Capabilities
0:31
The 'VL' (Vision-Language) capability allows the model to parse complex documents, read dashboard screenshots, and interpret scanned pricing sheets. (00:31, 01:54)
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Automated Billing Investigation
1:49
The agent was prompted to investigate a 30% increase in AI billing costs. It read the dashboard metrics (Spend up 30%, tickets up 3%), analyzed the price list, and pinpointed the cause: a switch to a thinking model using over 100 times more thinking tokens daily. (01:09, 02:01)
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Code Generation and Proposal
The agent autonomously wrote Python code to apply a defined quality bar, comparing routing options, and generated a formal proposal that projected staying within quality standards while saving approximately $100,000 monthly. (02:09, 02:16)
Technical details
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Model Specifications
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Nemotron 3.5 Super VL is an open-source model with 120 billion total parameters and 12 billion active parameters. It is designed for fast, efficient agent execution. (00:00)
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Multimodal Reasoning Workflow
146s
The agent workflow involves: 1) Reading dashboard metrics from a screenshot (e.g., identifying a 30% spend increase). 2) Parsing structured data from a scanned image (price list). 3) Identifying the root cause (e.g., excessive thinking token use). 4) Generating and executing Python code to optimize routing based on a quality bar. (01:46, 02:09)
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Deployment Environment
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The model is capable of running on a single NVIDIA DGX Spark. (00:00)
Mentioned resources
Channel & topics
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