Salesforce , which is customizing Nemotron to power AI agents that digitize and automate complex government workflows in secure customer environments through Missionforce Operations.
Synopsys software for semiconductor design verification interoperates with the model.
Nemotron 3.5 Super VL’s vision language strengths are targeted at what agents encounter in practice: optical character recognition, tables, charts and structured information in visual documents.
It processes a web page, extracts text, reads graphs and charts, and returns insights from what it finds. The same applies to PDFs and other documents agents commonly work with.
Nemotron 3.5 Super VL understands images, webpages and PDFs and acts on that understanding. Combining text and visual understanding in one model means agents don’t need to route tasks between separate systems — context carries through as work moves from document to data to decision.
Openness and customizability reinforce each other. The model ships with open weights, open datasets and training recipes.
Control runs in two directions. Organizations can run the model wherever their security and data-residency requirements dictate. They also choose to adopt a model they can adapt, rather than one subject to a provider’s restrictions.
In insurance, for example, a customized agent can scan claims for fraud markers on repeat. In financial services, an agent can review credit scores and flag activity against an organization’s own lending criteria. The model stays on premises. The data stays private.
Customization is supported through NVIDIA NeMo, Python-based pipelines and open datasets.
Nemotron 3.5 Super VL was trained on about 26 trillion tokens across pretraining and continued pretraining, then post-trained with roughly 3.8 million samples and reinforcement learning across dozens of environments.
NVIDIA is also releasing open datasets on Hugging Face spanning agentic tool use , finance, instruction following, long context, fairness and vision-language understanding, along with the reinforcement learning environments in NeMo Gym.
NVIDIA NIM packages the model as a production-ready microservice with performance-optimized, validated configurations and enterprise-grade support, refreshed every two weeks with security patches and performance improvements.
And with the model running on NVIDIA Dynamo, organizations can serve up to 8x more users with 85% lower latency.
Nemotron 3.5 Super VL is available now on Hugging Face, Open Router, ModelScope community and as NVIDIA NIM.
Nemotron 3.5 Super VL runs across NVIDIA’s full hardware stack: on DGX Spark , DGX Station , RTX PRO and RTX systems for local intelligence at the desktop, on NVIDIA Jetson at the edge, and across data center and cloud environments.
Nemotron 3.5 Bolt 74B-A9B 24G B is available now on Hugging Face, with support for llama.cpp, Ollama , LM Studio , Unsloth , and vLLM . Built for NVIDIA RTX Spark laptops arriving Oct. 16, it also runs on GeForce RTX PCs , DGX Spark , DGX Station and RTX PRO workstations .
Download the open weights , explore customization resources with NVIDIA NeMo, or read the technical blog for the full architecture breakdown.
Stay up to date on agents, NVIDIA Nemotron and more by subscribing to NVIDIA news , joining the community , and following NVIDIA AI on LinkedIn , Instagram , X and Facebook .
Explore self-paced video tutorials and livestreams .
Key considerations
- Investor positioning can change fast
- Volatility remains possible near catalysts
- Macro rates and liquidity can dominate flows
Reference reading
- https://blogs.nvidia.com/blog/nemotron-3-5-super-vl/#primary
- https://blogs.nvidia.com/blog/author/karibriski/
- https://blogs.nvidia.com/blog/nemotron-3-5-super-vl/#disqus_thread
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Informational only. No financial advice. Do your own research.