NVIDIA Nemotron 3.5: Open AI Models for Every Enterprise Tier

The arrival of NVIDIA’s Nemotron 3.5 family forces a clear decision for business leaders: continue renting black-box cloud AI, or invest in open models you shape and own. Nemotron 3.5 Super VL, a 120-billion-parameter model, is open and available for organizations ready to deploy always-on agents. Unlike proprietary models, Super VL is fully transparent — enterprises can post-train it on their own data, deploy it on-premises, and oversee every aspect of their agent workflows. The pressure to act intensifies as more companies apply open AI to industry-specific tasks, raising the bar for speed, accuracy, and compliance.

For CIOs and CTOs, the question isn’t just about performance anymore; it’s about data control, privacy, and the ability to adapt models as regulations or workflows evolve. Open models like Super VL let enterprises avoid vendor lock-in and maintain full oversight, a key advantage for sectors under strict data governance.

NVIDIA positions Nemotron 3.5 Super VL as the fastest open agentic model in its class — clocking throughput up to five times higher than comparable models, according to results from the Artificial Analysis Intelligence Index leaderboard. Even with a parameter count one-fifth the size of NVIDIA’s largest reasoning model, Super VL matches their accuracy on complex multimodal workflows, such as instruction following and document understanding. This means businesses can deploy sophisticated agents capable of handling days-long workflows without excessive hardware requirements or latency.

On the edge, the new Bolt models fill the gaps. Bolt 74B-A9B is tailored for local agent tasks — running natively with 2-bit quantization to squeeze into 24GB of memory, suitable for on-premises NVIDIA RTX or DGX systems. For resource-constrained environments, Bolt E4B 4GB brings conversational and interactive AI to devices like Jetson, powering voice assistants or embedded applications with impressive efficiency. Bolt models allow organizations to keep workloads local, reducing cloud costs and sidestepping latency and privacy concerns.

Nemotron 3.5 Super VL isn’t a one-size-fits-all solution. Its open architecture means businesses across finance, healthcare, security, and engineering are already post-training and fine-tuning the model for specialized agentic tasks. Examples span from EY using it for M&A due diligence, to Ramp’s financial document intelligence, and R1’s medical claims analysis. AI21 and Alice have tailored it for cybersecurity attack resilience, while Mercor optimized it for coding agents using the APEX-SWE benchmark.

Enterprise platform companies like Atlassian, CrowdStrike, and Salesforce have begun integrating Super VL to digitize workflows, automate security, or power agentic search. The common thread: the ability to mold the model with proprietary data and workflows, enhancing accuracy on the unique tasks that set each company apart. Organizations that move early can achieve significant process automation and insight extraction ahead of their competitors.

A key differentiator for Nemotron 3.5 Super VL is its blend of text and visual understanding. In practical terms, this means enterprise agents can process PDFs, scan tables and graphs, extract structured information, and analyze web pages — all within a single, unified model. There’s no need to shuffle data between separate systems for OCR, chart analysis, or document parsing. The result is less friction, improved accuracy, and faster cycle times for complex, data-heavy workflows.

Equally important, the open nature of Nemotron 3.5 ensures organizations retain control over where and how the model runs. With open weights, datasets, and recipes, businesses decide the infrastructure, security posture, and compliance boundaries. This is a material advantage for leaders in regulated industries or with stringent data residency requirements. For many, it tips the scale away from black-box cloud AI and toward a future that’s both open and bespoke.