Deploying NVIDIA Nemotron Models on Amazon EKS and SageMaker HyperPod

The landscape of agentic artificial intelligence demands models that go beyond standard text generation. Enterprises require large language models capable of complex reasoning, native tool utilization, and multimodal processing. The NVIDIA Nemotron 3.5 Super VL model, boasting a massive 120B-parameter mixture-of-experts architecture, directly addresses this need. However, deploying and scaling such heavy architectures in production environments poses significant infrastructure challenges.

This review explores the architectural nuances of Nemotron 3.5 Super VL, evaluates its performance in multi-step agentic tasks, and provides a production-grade deployment walkthrough on Amazon EKS and Amazon SageMaker HyperPod EKS using modern inference engines like vLLM and NVIDIA Dynamo. For developers seeking to bypass complex infrastructure orchestration while maintaining high throughput, leveraging an aggregator like n1n.ai offers an immediate alternative to direct server management.

Understanding the Nemotron 3.5 Super VL Architecture

NVIDIA's Nemotron 3.5 Super VL is a mixture-of-experts (MoE) model engineered specifically for agentic workflows. Unlike dense models that activate all parameters for every token, the MoE approach routes tokens through specific expert sub-networks, drastically reducing computational overhead during inference while maintaining high accuracy.

In agentic loops—where an LLM must plan, execute code, call external tools, and analyze visual or textual outputs iteratively—latency < 50ms per step is crucial. Nemotron's sparse routing architecture achieves this efficiency, making it ideal for autonomous multi-agent systems.

Infrastructure Prerequisites: EKS vs. SageMaker HyperPod

When deploying a 120B-parameter MoE model, standard Kubernetes clusters often fall short of memory bandwidth and inter-node communication requirements. Enterprise architects typically choose between two primary AWS deployment paradigms:

1. Amazon EKS with EKS Auto Mode: Best for organizations wanting simplified cluster management, automated node scaling, and integrated security policies without manually managing worker node groups.
2. Amazon SageMaker HyperPod EKS: Designed for massive scale deep learning, offering automated resilience, rapid cluster recovery, and optimized distributed training and inference runtimes across hundreds of GPUs.

Regardless of your chosen infrastructure, you will require GPU instances equipped with high-speed networking, such as AWS EC2 P5 instances featuring NVIDIA H100 GPUs or P4de instances featuring A100 GPUs.

Step-by-Step Deployment Guide with vLLM and NVIDIA Dynamo

To maximize throughput and minimize memory footprint, we deploy Nemotron 3.5 Super VL using vLLM combined with NVIDIA Dynamo for tensor parallelism and optimized kernel execution.

Step 1: Prepare the Kubernetes Manifest

Create a deployment configuration file named nemotron-deployment.yaml. Ensure your cluster has the NVIDIA Device Plugin installed for GPU resource allocation.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nemotron-vllm-server
  namespace: ai-inference
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nemotron-vllm
  template:
    metadata:
      labels:
        app: nemotron-vllm
    spec:
      containers:
      - name: vllm-container
        image: vllm/vllm-openai:latest
        command: ["python3