NVIDIA Nemotron 3.5 Super VL joins the NVIDIA Nemotron 3 Nano, Nano Omni, Super, Ultra, and Nemotron 3.5 Lightning models already available on AWS. It’s a 120B-parameter mixture-of-experts (MoE) model that activates 12B parameters per token. It combines reasoning and tool use with text, image, document, and video understanding, so one model serves both text and visual tasks. This post explores its architecture and use cases, then shows how to deploy it on Amazon Elastic Kubernetes Service (Amazon EKS), Amazon EKS Auto Mode, and Amazon SageMaker HyperPod EKS.

NVIDIA Nemotron 3.5 Super VL is an open multimodal model distilled from Nemotron 3 Ultra. NVIDIA publishes the weights, training datasets, and post-training recipes, so you can customize the model for domain-specific workloads and run it on infrastructure you control. Model details:

NVIDIA reports 63.44 percent on Hermes Bench and 87.05 percent on LangChain agent evaluations, and 80.8 percent on the CharXiv chart-reasoning benchmark. (technical report)

Nemotron 3.5 Super VL uses a hybrid Mamba-Transformer MoE architecture, interleaving Mamba-2, attention, and expert layers. Sparse routing activates 12B of the 120B parameters per token, so compute per token matches a much smaller model while GPU memory still holds the full weights. Mamba-2 layers carry a constant-size state during generation, so their memory does not grow with sequence length the way KV cache does, which keeps long contexts affordable. A recursively shared Multi-Token Prediction (MTP) head supports native speculative decoding.

Nemotron 3.5 Super VL supports agent workflows that combine reasoning with visual understanding:

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