NVIDIA Super POD

Project Overview

NVIDIA Super POD is an open-source AI infrastructure project that recreates many of the core components found in modern GPU-powered AI platforms. The project provides a self-service environment for provisioning, operating, monitoring, and scheduling GPU workloads on cloud infrastructure.

The platform was designed to explore the operational challenges of large-scale AI systems, including GPU orchestration, model serving, observability, resource scheduling, and cost optimization. Built entirely on AWS using Infrastructure as Code, the project serves as a playground for experimenting with production-grade AI infrastructure patterns.

The goal is to provide a reproducible foundation for hosting LLMs, running inference workloads, benchmarking models, and exploring distributed AI infrastructure without requiring dedicated on-premises hardware.

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Key Features

Automated GPU Cluster Provisioning

Provisioned GPU-enabled Kubernetes clusters on AWS using Terraform, enabling repeatable and reproducible infrastructure deployments.

NVIDIA GPU Platform

Integrated NVIDIA GPU Operator to automate driver installation, device management, and GPU lifecycle operations across Kubernetes nodes.

Production-Grade Observability

Implemented GPU monitoring using NVIDIA DCGM Exporter, Prometheus, Grafana, and Alertmanager, providing deep visibility into cluster utilization and performance.

Multi-Model Inference Serving

Configured NVIDIA Triton Inference Server to host and serve multiple machine learning models concurrently from a shared GPU infrastructure.

HPC-Style Job Scheduling

Implemented SLURM and Enroot to support batch workloads, distributed jobs, and GPU resource scheduling similar to traditional supercomputing environments.

Cost-Optimized Compute

Leveraged AWS Spot GPU instances to significantly reduce operational costs while maintaining flexible compute capacity.

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My Contributions

• Designed and provisioned GPU-enabled AWS infrastructure using Terraform.
• Built Kubernetes clusters optimized for AI and machine learning workloads.
• Installed and configured NVIDIA GPU Operator across cluster nodes.
• Integrated DCGM Exporter for GPU telemetry and performance monitoring.
• Designed Grafana dashboards visualizing GPU utilization, memory consumption, power usage, and inference workloads.
• Deployed Triton Inference Server for multi-model inference serving.
• Implemented SLURM-based job scheduling and workload orchestration.
• Configured Enroot container runtimes for HPC-style workloads.
• Automated deployment, monitoring, and cluster management workflows.
• Documented infrastructure architecture and operational best practices.

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Technical Highlights

AI Infrastructure Engineering

Designed infrastructure specifically optimized for machine learning and LLM workloads rather than general-purpose cloud applications.

GPU Resource Management

Implemented automated GPU provisioning, monitoring, and scheduling mechanisms capable of supporting multiple concurrent workloads.

Production-Ready Model Serving

Built a scalable inference platform capable of serving multiple models through Triton Inference Server while maximizing GPU utilization.

End-to-End Observability

Established monitoring pipelines that expose GPU metrics, node health, workload performance, and infrastructure utilization in real time.

HPC Meets Kubernetes

Combined modern Kubernetes orchestration with traditional high-performance computing concepts through SLURM scheduling and GPU-aware resource allocation.

Cost Optimization

Leveraged Spot Instances and automated scaling strategies to minimize infrastructure costs while maintaining access to GPU resources.

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Challenges & Solutions

Challenge

GPU infrastructure is expensive, operationally complex, and often difficult to reproduce outside large AI organizations. Building a platform that supports model serving, scheduling, monitoring, and experimentation requires coordinating multiple layers of infrastructure.

Solution

Created an automated infrastructure stack combining Terraform, Kubernetes, NVIDIA GPU tooling, Triton Inference Server, and HPC scheduling technologies into a reproducible and cost-efficient platform.

Outcome

Delivered a production-like AI infrastructure environment capable of hosting LLMs, benchmarking inference workloads, exploring distributed AI systems, and experimenting with GPU resource management at scale.

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Technology Stack

Infrastructure Terraform, AWS, Spot Instances

Container Platform Kubernetes, Docker

GPU Platform NVIDIA GPU Operator, CUDA, DCGM

Monitoring Prometheus, Grafana, Alertmanager

Inference Serving NVIDIA Triton Inference Server

Scheduling SLURM, Enroot

AI Workloads LLM Inference, Model Serving, Distributed AI

Domain AI Infrastructure, GPU Computing, High Performance Computing (HPC), Cloud-Native AI Platforms