NVIDIA launches open 120B multimodal agent model and two local models
NVIDIA launches open 120B multimodal agent model and two local models
Open Source AI

NVIDIA launches open 120B multimodal agent model and two local models

NVIDIAOct 15, 2026

NVIDIA released Nemotron 3.5 Super VL and a 24GB Bolt model, while a smaller 4GB Bolt conversational model is scheduled for October 23.

NVIDIA announced Nemotron 3.5 Super VL on October 15, 2026, describing it as an open, 120-billion-parameter model for multimodal agent tasks. The company says it is designed to handle instruction following, tool use, business workflows and visual understanding, including reading information in documents, webpages, charts and tables. NVIDIA says the model completes tasks five times faster than the next-best model in its class, and reports accuracy comparable to its largest reasoning model at roughly one-fifth the parameter count.

The release also adds two models aimed at local deployment. Nemotron 3.5 Bolt 74B-A9B 24GB uses native 2-bit quantization to fit within 24GB of memory and is intended for agent workloads on NVIDIA RTX and DGX Spark systems. NVIDIA says it is available now on Hugging Face and supports tools including llama.cpp, Ollama, LM Studio, Unsloth and vLLM. Nemotron 3.5 Bolt E4B 4GB is designed for conversational and physical agents on GeForce RTX, Jetson and RTX/DGX Spark hardware; NVIDIA says it is expected on October 23.

Super VL is available now through Hugging Face, OpenRouter, ModelScope and NVIDIA NIM, and is designed to run across NVIDIA data-center, workstation and edge hardware. NVIDIA says organizations can adapt it using their own data, tools and workflows. The release includes open weights, datasets and training recipes; customization is supported through NVIDIA NeMo and Python-based pipelines.

NVIDIA reports that Super VL was trained on about 26 trillion tokens and post-trained with roughly 3.8 million samples, alongside reinforcement learning across dozens of environments. The company lists applications including finance-document analysis, cybersecurity, legal research and coding. It also names organizations customizing or integrating the model, among them Atlassian, CrowdStrike, Perplexity and Salesforce. These are company-reported examples, rather than independent evaluations of the models’ performance.

The three releases extend NVIDIA’s Nemotron lineup from data-center deployments toward local systems. Super VL and Bolt 74B-A9B are available now; the smaller Bolt model has a stated October 23 release date.

Industry Impact

More options for enterprise AI deployment

Enterprise software, finance, cybersecurity, legal services, and software development

Overview

NVIDIA’s release gives organizations a new open-weight multimodal model for agent workflows, plus smaller models designed for local systems. Developers and ML engineers can evaluate the models for document analysis, visual understanding, and tool use, while business leaders can consider adapting them to internal processes. Local deployment may offer more flexibility over where workloads run, but teams will need to test performance, hardware fit, and operating costs. NVIDIA’s speed and accuracy claims are company-reported, not independent evaluations.

Key Impact Points

Super VL supports multimodal agent tasks such as interpreting documents, charts, and tables.

The 24GB Bolt model targets local agent workloads on NVIDIA RTX and DGX Spark systems.

Open weights, datasets, and training recipes provide materials for organizations to customize models.

Opportunities

Customize AI for internal workflows

Enterprise operations, finance, cybersecurity, legal services, and software development

Organizations with document-heavy or tool-driven processes can evaluate Nemotron 3.5 Super VL for workflows involving visual information and agent actions. Its open weights, datasets, and training recipes, alongside customization through NVIDIA NeMo and Python-based pipelines, offer a starting point for adapting a model to internal data and tools. The Bolt models also create an option to test local deployment on supported NVIDIA systems. Commercial value will depend on measured accuracy, integration effort, hardware availability, and operating costs; NVIDIA’s reported performance figures should be independently validated before production use.

  • Build customized document and workflow agents

    Problem: Business processes involving documents, charts, webpages, and internal tools can require substantial manual review.

    Solution: Evaluate and adapt Nemotron 3.5 Super VL with organization-specific data, tools, and workflows.

    Target Users: Enterprise AI teams and developers in finance, cybersecurity, legal services, and software development.

    Business Value: Potentially streamline selected analysis and workflow tasks, subject to validation against existing processes.

    Revenue Potential: Possible savings from internal efficiency or commercial workflow offerings; the article provides no revenue estimates.

Reference link

https://blogs.nvidia.com/blog/nemotron-3-5-super-vl

Image Source Link

https://blogs.nvidia.com/wp-content/uploads/2026/10/FIRST-agentic-ai-nemotron-3.5-super-vl-blog-1920x1080-5783765-1280x720.jpeg

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