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

The landscape of enterprise artificial intelligence is shifting rapidly from static chat interfaces to dynamic, autonomous agents capable of complex reasoning, tool execution, and multimodal perception. To power these demanding workloads, organizations require foundation models that deliver low latency, high throughput, and robust instruction-following capabilities. The introduction of the NVIDIA Nemotron 3.5 Super VL model represents a significant milestone in this domain. As a massive 120 billion-parameter mixture-of-experts architecture, it combines advanced multi-step reasoning and precise tool use with rich multimodal understanding. However, deploying and scaling models of this magnitude in production presents formidable engineering challenges. This comprehensive guide explores the architecture of Nemotron models and demonstrates how to optimize their deployment on Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon SageMaker HyperPod EKS using high-performance serving engines like vLLM and NVIDIA Dynamo.

Understanding NVIDIA Nemotron Architecture and Agentic Capabilities

NVIDIA Nemotron models are engineered specifically for enterprise workloads that demand strict governance, factual accuracy, and high-speed execution. The 120B-parameter mixture-of-experts (MoE) design optimizes computational efficiency by routing tokens through a subset of active parameters, drastically reducing the inference cost without sacrificing model capacity.

In agentic workflows, an LLM acts as the orchestrator. It must analyze user inputs, formulate execution plans, invoke external APIs or databases, parse the output, and iteratively correct its path. Nemotron excels in this environment due to its rigorous training in system prompting, structured JSON generation, and function calling. Furthermore, its multimodal capabilities allow it to ingest unstructured visual data, such as system architecture diagrams, UI mockups, or telemetry charts, and translate those visual inputs directly into actionable API calls or code blocks.

When building applications that require multiple model calls or fallback mechanisms across different providers, managing infrastructure in-house can quickly become a bottleneck. Many developers prefer to prototype and scale their agentic pipelines using high-performance API aggregators like n1n.ai, which provide unified access to cutting-edge models with enterprise-grade reliability and low latency.

Infrastructure Prerequisites for Enterprise Deployment

Deploying a 120B-parameter MoE model requires careful hardware selection and cluster configuration. To achieve acceptable token generation speeds and keep time-to-first-token < 500ms, you need distributed inference setups spanning multiple GPUs.

Hardware Sizing Guidelines

• Model Weight Precision: FP8 or INT4 quantization is highly recommended to fit the model within multi-GPU nodes while maximizing memory bandwidth.
• Node Configuration: Amazon EC2 instances powered by NVIDIA H100 or H200 GPUs (such as p5.48xlarge or p5en.48xlarge) provide the necessary NVLink bandwidth for high-speed tensor parallelism.
• Cluster Orchestration: Amazon EKS combined with EKS Auto Mode simplifies node provisioning, scaling, and network configuration, ensuring seamless operation under fluctuating enterprise traffic.

Deploying Nemotron on Amazon EKS with vLLM

To serve NVIDIA Nemotron efficiently, vLLM offers optimized memory management through PagedAttention, which virtually eliminates memory fragmentation and significantly boosts concurrent request throughput.

Step 1: Setting up the EKS Cluster and Node Groups

First, ensure your Amazon EKS cluster is provisioned with GPU-accelerated node groups. You can use eksctl or Terraform. Below is an excerpt of a Kubernetes manifest for deploying the vLLM inference server for Nemotron.

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