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

The landscape of artificial intelligence is rapidly shifting from passive text generation to proactive, autonomous agentic workflows. Enterprises are no longer satisfied with models that simply answer prompts; they demand systems capable of planning, executing multi-step tool calls, processing multimodal inputs, and reasoning through complex domains. To meet these rigorous demands, NVIDIA introduced the Nemotron family of models, culminating in the advanced NVIDIA Nemotron 3.5 Super VL. As a 120B-parameter mixture-of-experts (MoE) powerhouse, this model bridges the gap between advanced reasoning, deterministic tool use, and deep multimodal understanding. However, deploying and scaling a 120B-parameter model with complex MoE routing and multimodal ingestion presents substantial infrastructure challenges for DevOps and machine learning engineering teams.

This comprehensive technical guide explores the architectural nuances of NVIDIA Nemotron 3.5 Super VL, details its integration into enterprise agentic workflows, and provides a production-grade deployment walkthrough on Amazon EKS, EKS Auto Mode, and Amazon SageMaker HyperPod EKS using high-throughput serving engines like vLLM and NVIDIA Dynamo. For developers looking to bypass the operational overhead of managing multi-node GPU clusters, we will also examine how aggregator platforms like n1n.ai provide instant, highly optimized API access to frontier models with latency < 50ms.

Architectural Deep Dive into NVIDIA Nemotron 3.5 Super VL

Understanding the internals of NVIDIA Nemotron 3.5 Super VL is essential for optimizing its inference footprint. Unlike dense architectures that activate every parameter for every single token generated, Nemotron 3.5 Super VL leverages a Mixture-of-Experts routing mechanism. While it boasts 120 billion total parameters, only a fraction of those parameters are active during any given forward pass. This sparse activation drastically reduces compute overhead per token while preserving the representation capacity of a massive model.

+------------------------------------------------------------+
|                NVIDIA Nemotron 3.5 Super VL                |
+------------------------------------------------------------+
| Multimodal Vision Encoder <--> Cross-Attention Fusion Layer|
+------------------------------------------------------------+
| Sparse Mixture-of-Experts (MoE) Routing Engine             |
|  - Expert 1  - Expert 2  - Expert 3  - Expert 4            |
+------------------------------------------------------------+
| Tool-Use Execution & Reasoning Core                        |
+------------------------------------------------------------+

Key architectural highlights include:

1. Multimodal Fusion: The vision encoder is tightly coupled with the transformer backbone via cross-attention layers, allowing the model to reason natively over interleaved text and high-resolution visual inputs without suffering from modality misalignment.
2. Deterministic Tool Routing: Nemotron is fine-tuned specifically for agentic frameworks like LangChain, AutoGen, and semantic kernel architectures. Its token output includes structured JSON schemas that map cleanly to external API endpoints.
3. KV Cache Optimization: Due to the long context windows required for agentic workflows (often exceeding 32k tokens), memory bandwidth becomes the primary bottleneck. Techniques likePagedAttention via vLLM are mandatory to prevent out-of-memory (OOM) errors during heavy multi-turn conversations.

For engineering teams that need to test agentic workflows immediately without provisioning Kubernetes clusters, connecting to n1n.ai offers a streamlined alternative. You can route your LangChain agents through a unified endpoint instantly.

Infrastructure Prerequisites on AWS

Deploying a 120B MoE model demands robust underlying infrastructure. Whether you choose Amazon EKS with GPU-accelerated node groups or Amazon SageMaker HyperPod EKS, your cluster must satisfy specific hardware and networking constraints.

Hardware Requirements

• Instance Types: p5.48xlarge (featuring 8x NVIDIA H100 GPUs with 80GB HBM3 each) or p4de.24xlarge (featuring 8x NVIDIA A100 GPUs with 40GB HBM2e each).
• Interconnect: Elastic Fabric Adapter (EFA) enabled for high-speed, low-latency inter-node communication during tensor and pipeline parallel execution.
• Storage: High-throughput Amazon FSx for Lustre or EBS gp3 volumes configured for rapid weight loading during pod initialization.

Configuring Amazon EKS Auto Mode

Amazon EKS Auto Mode simplifies cluster management by automatically provisioning and scaling compute resources. When deploying heavy workloads like NVIDIA Nemotron 3.5 Super VL, you must configure your NodeClasses to target GPU-optimized instances.

apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: gpu-nodepool
spec:
  template:
    spec:
      nodeClassRef:
        name: eks-auto-gpu-class
      requirements:
        - key: node.kubernetes.io/instance-type
          operator: In
          values: ["p5.48xlarge