NVIDIA Nemotron 3.5 Super VL arrives on AWS
AWS announced that NVIDIA Nemotron 3.5 Super VL is now available alongside the existing Nemotron models on its platform. The 120B-parameter multimodal MoE model activates 12B parameters per token, supports text, image, document and video inputs, and can be deployed through Amazon EKS, EKS Auto Mode and SageMaker HyperPod EKS.
AWS provides a working deployment path for an open model that combines reasoning, tool use and visual understanding, giving developers a concrete option for multimodal agent workloads on managed Kubernetes infrastructure.
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.
About Nemotron 3.5 Super VL
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:
- 120B total parameters, 12B active per token.
- Hybrid Mamba-2, MoE, and attention layers with a RADIO vision encoder.
- Context length up to 1M tokens.
- Input: text, image, document, and video.
- Output: text.
- BF16 and NVFP4 checkpoints.
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