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

Deploying massive multimodal large language models for production-grade agentic workflows remains one of the most demanding engineering challenges in enterprise infrastructure today. The introduction of the NVIDIA Nemotron 3.5 Super VL model, boasting a massive 120B-parameter mixture-of-experts (MoE) architecture, changes the landscape by combining advanced multi-step reasoning, native tool utilization, and deep multimodal understanding. However, orchestrating such a heavy workload requires robust container orchestration and optimized inference runtimes. This guide dives deep into deploying Nemotron models across Amazon EKS, EKS Auto Mode, and Amazon SageMaker HyperPod EKS, leveraging high-performance engines like vLLM and NVIDIA Dynamo.

Understanding NVIDIA Nemotron 3.5 Super VL Architecture

The NVIDIA Nemotron 3.5 Super VL is engineered explicitly for complex agentic workflows where a model must perceive visual context, plan a sequence of actions, call external APIs, and synthesize intermediate results. Unlike standard dense models, the 120B MoE layout activates only a fraction of its total parameters per token, drastically reducing computational overhead during inference while maintaining exceptional reasoning fidelity.

For enterprise architects, managing this model locally or on unoptimized clusters introduces severe latency bottlenecks and memory fragmentation issues. To achieve production SLAs with latency < 200ms per token generation, specialized serving stacks are mandatory. This is where combining AWS infrastructure with optimized model routing via platforms like n1n.ai can significantly streamline development. While self-hosting handles core heavy workloads, routing dynamic fallback or lightweight validation calls through n1n.ai ensures maximum availability and cost efficiency.

Prerequisites and Infrastructure Preparation

Before provisioning your Kubernetes manifests, ensure your AWS environment meets the strict hardware requirements for a 120B-parameter MoE model. You will typically need multi-node GPU instances such as p4d.24xlarge or p5.48xlarge equipped with NVIDIA A100 or H100 Tensor Core GPUs.

Setting Up Amazon EKS and EKS Auto Mode

Amazon EKS Auto Mode simplifies cluster management by automating the provisioning of underlying compute nodes, storage, and networking layers. Here is a baseline Terraform snippet or Kubernetes configuration outline to get your GPU-accelerated node groups running:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nemotron-vllm-deployment
  namespace: ai-inference
spec:
  replicas: 2
  selector:
    matchLabels:
      app: nemotron-vllm
  template:
    metadata:
      labels:
        app: nemotron-vllm
    spec:
      containers:
      - name: vllm-server
        image: vllm/vllm-openai: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=0.90",
          "--max-model-len=8192"
        ]
        ports:
        - containerPort: 8000

Deploying on Amazon SageMaker HyperPod EKS

For enterprise teams scaling multi-node training and massive inference pipelines, Amazon SageMaker HyperPod EKS provides a purpose-built infrastructure layer. HyperPod automates fault recovery, node health checks, and deep learning framework integration.

When deploying NVIDIA Nemotron on SageMaker HyperPod EKS, integrating NVIDIA Dynamo helps manage dynamic request batching and memory allocation across distributed GPU clusters. Below is a comparative overview of how different deployment targets handle performance metrics:

Optimizing Inference with vLLM and NVIDIA Dynamo

Running large MoE models like Nemotron efficiently requires advanced memory management techniques such as PagedAttention, which is natively supported by vLLM. Furthermore, NVIDIA Dynamo optimizes kernel execution and tensor communication across NVLink fabrics.

When building agentic loops—where the LLM continuously calls tools and processes iterative outputs—network latency between your application backend and the model endpoint becomes critical. Integrating an API aggregator like n1n.ai into your multi-model routing strategy allows your application to seamlessly switch between your self-hosted Nemotron deployment on EKS and other frontier models depending on the specific task complexity.

Python Integration Example

Once your endpoint is active on EKS or HyperPod, you can interact with it using standard OpenAI-compatible client libraries. Here is how you can configure your Python code to query your deployment or fallback to an aggregated endpoint:

from openai import OpenAI

# Configure client pointing to your EKS internal load balancer or n1n.ai proxy
client = OpenAI(
    base_url="http://your-eks-load-balancer-address/v1",
    api_key="your-cluster-api-key"
)

response = client.chat.completions.create(
    model="nvidia/nemotron-3.5-super-vl",
    messages=[
        {
            "role": "user",
            "content": "Analyze this system architecture diagram and suggest three performance bottlenecks."
        }
    ],
    max_tokens=1024,
    temperature=0.2
)

print(response.choices[0].message.content)

Conclusion and Production Best Practices

Deploying NVIDIA Nemotron 3.5 Super VL on Amazon EKS and SageMaker HyperPod EKS unlocks unprecedented capabilities for agentic AI systems. By leveraging vLLM for memory optimization and NVIDIA Dynamo for high-speed tensor routing, enterprises can achieve low-latency, high-throughput inference at scale. To further optimize costs and build resilient architectures, consider combining your on-cluster deployments with flexible multi-provider routing solutions.

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