# Accelerate agentic AI with NVIDIA Nemotron models on Amazon EKS and SageMaker HyperPod EKS

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/accelerate-agentic-ai-with-nvidia-nemotron-models-on-amazon-eks-and-sagemaker
> **Original Source**: [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/accelerate-agentic-ai-with-nvidia-nemotron-models-on-amazon-eks-and-sagemaker-hyperpod-eks/)
> **Author**: Alex Iankoulski (undefined)
> **Published Date**: Thursday, October 29, 2026
> **Category**: RESEARCH
> **Tags**: AI, AWS ML Blog, research

## Executive Summary
NVIDIA Nemotron 3.5 Super VL is a 120B-parameter mixture-of-experts model that pairs reasoning and tool use with multimodal understanding. This post explores its architecture and use cases, then shows how to deploy it on Amazon EKS, EKS Auto Mode, and Amazon...



## Full Coverage
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 )

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