Nvidia Nemotron 3.5 Super VL Delivers 5x Faster Performance for Enterprise Agentic AI Deployment

Nvidia Nemotron 3.5 Super VL Redefines Speed for Enterprise Agentic AI

Nvidia has launched Nemotron 3.5 Super VL, an open-weight AI model with 120 billion parameters designed to accelerate enterprise agent tasks. The model completes tasks 5x faster than its nearest competitor while maintaining accuracy comparable to Nvidia's largest reasoning models at roughly one-fifth the parameter count1

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. Built in collaboration with the Nemotron Coalition, Super VL targets the growing demand for agentic AI systems that can handle instruction following, tool use, multistep business workflows, and visual understanding2

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. According to Artificial Analysis benchmark testing, Super VL delivers approximately 625 output tokens per second on four Blackwell B200 GPUs, significantly outpacing competitors like InclusionAI's Ling 3.0 Flash and DeepSeek's V4.1 Flash4

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. The model's speed advantage becomes critical for enterprises running always-on agents across workflows spanning days or weeks, where task completion speed directly impacts operational efficiency.

Post-Training Capabilities Transform Specialized Performance

The true power of Nemotron 3.5 Super VL emerges through post-training on proprietary data. Organizations customizing the model have reported accuracy gains ranging from 9% to 44% across specialized tasks4

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. In medical insurance claims processing, post-training boosted accuracy from roughly 28% to 72%, surpassing Claude Opus 5's 38% performance on the same evaluation1

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. For cybersecurity attack resilience, the model improved from 35% to 76% accuracy, compared to GPT-6.1 Sol's 66%4

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. Scale AI has already post-trained the model for Skydance's vendor credit finance agent and EY's merger-and-acquisition due diligence agent, while AfterQuery customized it for Hebbia's credit agreement analysis2

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. Unlike closed frontier models, organizations can deploy Super VL on their own infrastructure, maintaining data control and security while tailoring the multimodal AI model to specific business requirements5

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Nemotron 3.5 Bolt Brings Autonomous Agent Capabilities to Local Hardware

Alongside Super VL, Nvidia released two Nemotron 3.5 Bolt variants for edge deployment and local hardware deployment. Bolt 74B-A9B uses native 2-bit quantization to fit within 24GB of memory, enabling it to run on NVIDIA RTX/DGX Spark systems alongside other applications2

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. This allows developers to delegate everyday agentic tasks like web research, writing, and debugging code without sending workloads to the cloud. The model ranks first among open AI models in its class on PinchBench and second for BrowseComp2

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. The smaller Bolt E4B, arriving October 23, occupies just 4GB of memory and targets physical AI systems running on GeForce RTX, Jetson, and RTX/GTX Spark3

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. Designed for conversational agents requiring fast local responses, Bolt E4B could power voice assistants, interactive characters, wearables, household devices, and car dashboards. The combination of Super VL and Bolt models creates a complete LLM family spanning data center to desktop deployments, giving organizations flexibility in choosing where their autonomous agents operate based on security, latency, and data sovereignty requirements.

US Open-Weight AI Models Challenge Chinese Dominance

The Nvidia Nemotron release represents a broader shift in the competitive landscape of open-weight AI models. Chinese labs like DeepSeek and Moonshot.ai have historically led in delivering open, customizable models, but recent launches from Western companies are closing that gap1

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. French lab Mistral's Le Chonk model recently achieved state-of-the-art benchmarks, while Reflection AI's Beam model generated significant attention1

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. This geographic shift gains importance following the OpenAI Hugging Face breach, which demonstrated that closed models aren't immune to security risks. If US-made open AI models prove competitive with Chinese alternatives, enterprises may feel more comfortable adopting them, particularly given cybersecurity guidance discouraging foreign technology in sensitive applications. The ability to post-train Super VL on approximately 26 trillion tokens during pretraining and 3.8 million samples during post-training, combined with reinforcement learning across 100 open environments, provides organizations with unprecedented customization depth5

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. Major platform companies including Atlassian, CrowdStrike, Dassault Systèmes, Perplexity, Salesforce, and Synopsys are already integrating Nemotron 3.5 Super VL for enterprise agent tasks ranging from agentic search to semiconductor design verification2

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. Watch how these customizable on-premise AI deployments perform beyond benchmark testing, as real-world validation will determine whether local hardware can deliver reliable results without sacrificing response speed or demanding excessive memory resources.