Accelerate Agentic AI with NVIDIA Nemotron Models on Amazon EKS and SageMaker HyperPod EKS

Building autonomous, reasoning-heavy AI agents requires underlying large language models that combine advanced chain-of-thought capabilities with robust tool use. NVIDIA Nemotron models—specifically the massive 120B-parameter mixture-of-experts variants—meet this demand by pairing deep multimodal understanding with high throughput. However, serving such large models efficiently in production presents significant orchestration challenges. In this technical review, we examine how to deploy and scale NVIDIA Nemotron on Amazon EKS, EKS Auto Mode, and Amazon SageMaker HyperPod EKS using optimized serving stacks like vLLM and NVIDIA Dynamo.

The Architecture of NVIDIA Nemotron for Agentic Workflows

Agentic workflows diverge heavily from traditional request-response chatbots. An agent must iteratively plan, generate tool calls, ingest external API responses, and correct its trajectory before returning a final answer. This iterative loop requires extremely low time-to-first-token (TTFT) and high token generation throughput.

The NVIDIA Nemotron architecture leverages a mixture-of-experts (MoE) design that activates only a subset of parameters per token, drastically reducing compute overhead without sacrificing capacity. When combined with advanced tool-use token schemas, Nemotron can accurately serialize structured JSON payloads for API integrations. Yet, managing memory footprints for a 120B model requires sophisticated tensor parallelism and pipeline parallelism, which can be difficult to configure manually across distributed Kubernetes clusters.

Infrastructure Strategy: Amazon EKS vs. SageMaker HyperPod EKS

When deploying frontier models at scale, platform engineers typically choose between standard Amazon EKS with advanced cluster autoscalers and Amazon SageMaker HyperPod EKS, which is purpose-built for massive distributed training and high-throughput inference workloads.

For most enterprise applications, standard EKS combined with Karpenter and EKS Auto Mode provides streamlined cluster operations. However, for continuous fine-tuning loops and multi-node vLLM tensor-parallel deployments of NVIDIA Nemotron, SageMaker HyperPod EKS delivers built-in resilience features that automatically detect hung nodes and replace them without breaking distributed inference rings.

Deploying NVIDIA Nemotron with vLLM and NVIDIA Dynamo

To achieve production-grade latency, raw PyTorch execution is insufficient. We leverage vLLM forPagedAttention memory management and NVIDIA Dynamo for dynamic compilation and execution graphs. Below is a comprehensive guide and Kubernetes manifest snippet to deploy Nemotron on your cluster.

Step 1: Setting up the Kubernetes Deployment Manifest

Ensure your EKS cluster is equipped with AWS Neuron or NVIDIA GPU operator (specifically supporting H100 or H200 instances with Elastic Fabric Adapter enabled). Here is an example Kubernetes Deployment configuration targeting a multi-GPU node pool:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nemotron-vllm-deployment
  namespace: ai-inference
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nemotron-vllm
  template:
    metadata:
      labels:
        app: nemotron-vllm
    spec:
      containers:
      - name: vllm-server
        image: nvcr.io/nim/nvidia/nemotron-3-5-super-vl:latest
        resources:
          limits:
            nvidia.com/gpu: "8"
            memory: "512Gi"
            cpu: "64"
          requests:
            nvidia.com/gpu: "8"
            memory: "256Gi"
            cpu: "32"
        command: ["python3", "-m", "vllm.entrypoints.openai.api_server"]
        args:
          - "--model=nvidia/Nemotron-3.5-Super-VL"
          - "--tensor-parallel-size=8"
          - "--gpu-memory-utilization=0.92"
          - "--max-model-len=8192"
          - "--port=8000"
        ports:
        - containerPort: 8000
        env:
        - name: HUGGING_FACE_HUB_TOKEN
          valueFrom:
            secretKeyRef:
              name: hf-secret
              key: token
        volumeMounts:
        - mountPath: /dev/shm
          name: shm
      volumes:
      - name: shm
        emptyDir:
          medium: Memory
          sizeLimit: 16Gi

Step 2: Optimizing with Python Client SDKs

Once your cluster endpoint is live, interacting with the model requires handling streaming reasoning tokens and tool calls. If you want to bypass complex local infrastructure management while testing various frontier models, developers frequently leverage API aggregators like n1n.ai for instant, unified access.

Here is a Python script demonstrating how to interface with an OpenAI-compatible endpoint hosting NVIDIA Nemotron:

import os
from openai import OpenAI

# Initialize client pointing to your EKS endpoint or n1n.ai aggregator
client = OpenAI(
    base_url="https://api.n1n.ai/v1",
    api_key=os.environ.get("N1N_API_KEY")
)

response = client.chat.completions.create(
    model="nvidia/nemotron-3.5-super-vl",
    messages=[
        {
            "role": "system",
            "content": "You are an advanced agentic AI assistant capable of tool execution."
        },
        {
            "role": "user",
            "content": "Analyze the latest sales metrics and generate a structured JSON report."
        }
    ],
    temperature=0.1,
    max_tokens=2048,
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="", flush=True)

Performance Optimization Pro Tips

1. Tensor Parallelism Tuning: For 120B MoE models, always align your tensor parallel size with the physical GPU interconnect boundaries within your Amazon EKS node groups. Using 8 GPUs per pod across an EFA fabric prevents PCIe bottlenecks.
2. KV Cache Quantization: Enable FP8 or INT8 KV cache quantization inside vLLM (--kv-cache-dtype fp8) to double your effective batch size and lower latency significantly during heavy multi-turn agent conversations.
3. Unified API Abstraction: Managing multiple self-hosted models alongside external proprietary APIs introduces operational debt. Integrating services like n1n.ai simplifies fallback routing and load balancing across providers.

Conclusion

Deploying NVIDIA Nemotron on Amazon EKS and SageMaker HyperPod EKS bridges the gap between massive multimodal capability and enterprise-grade reliability. By combining vLLM, NVIDIA Dynamo, and robust Kubernetes orchestration, engineering teams can build high-speed, cost-effective agentic workflows. For developers looking to streamline experimentation before full cluster deployment, get a free API key at n1n.ai.