GPU-ACCELERATED AI INFRASTRUCTURE
Designing and Operating GPU-Accelerated AI Infrastructure
Course Overview
This course provides a comprehensive, systems-level foundation for designing, deploying, and operating modern GPU-accelerated AI infrastructure. Across eight modules, participants progress from the fundamentals of AI workload behavior through GPU architecture, orchestration, high-performance networking, storage, multi-tenant scheduling, observability, and hybrid scaling strategy — building the full stack of knowledge required to architect production-grade AI platforms.
The course is built around real infrastructure decisions: how to size GPU clusters, how to design fabrics that keep GPUs fed, how to share expensive capacity fairly across teams, and how to keep a fleet observable and compliant at scale. Each module closes with applied case-study analysis grounded in real-world deployment scenarios.
Target Audience
This course is designed for infrastructure and platform professionals who are responsible for designing, deploying, or operating modern data centre environments and are transitioning toward AI and GPU-accelerated workloads, including:
- Data Centre and Infrastructure Engineers
- Systems Engineers and Technical Architects
- Platform Engineers supporting AI, ML, or analytics workloads
- Virtualization and Platform Administrators
- Cloud and Hybrid Infrastructure Engineers
- IT professionals involved in compute, networking, or storage architecture design
Prerequisites
Participants should have the following background before attending the course:
- Solid understanding of enterprise data centre concepts, including compute, networking, and storage fundamentals
- Server hardware architecture and operating systems knowledge (Linux preferred)
- Hands-on experience with virtualization or platform administration
- Basic familiarity with containerisation and Kubernetes concepts
- Working knowledge of IP networking concepts (VLANs, routing, switching)
- General understanding of storage technologies (block, file, performance characteristics)
Course Objectives
Upon successful completion of this course, participants will be able to:
- Explain the AI workload lifecycle and map infrastructure requirements across each phase
- Architect compute, network, and storage stacks optimised for AI infrastructure
- Configure and validate GPU-optimised hardware platforms and rack deployments
- Design and tune RDMA-enabled high-performance fabrics with proper congestion control mechanisms
- Implement NVMe-over-Fabrics (NVMe-oF) storage architectures and integrate them into Kubernetes environments
- Deploy MLOps and Generative AI pipelines on Kubernetes with comprehensive observability
- Design hybrid AI scaling strategies that align with compliance and cost objectives
- Produce portfolio-ready AI infrastructure design documents
Detailed Course Outline
AI Workload Lifecycle and Infrastructure Requirements
Establishes the foundation of AI infrastructure design by examining how AI workloads differ fundamentally from traditional enterprise applications, and how each lifecycle phase places distinct demands on compute, storage, and network resources.
- The AI Workload Lifecycle — Overview
- Why AI Workloads Break Traditional Infrastructure Assumptions
- Resource Requirement Mapping Per Lifecycle Phase
- Storage, Network, and Compute Demand Patterns Across Phases
- Performance Bottleneck Identification
- Use Cases: Training vs. Batch Inference vs. Real-Time Analytics
GPU Computing Platforms and Architecture for AI
A deep architectural examination of modern NVIDIA GPU platforms, from silicon-level design through multi-GPU topology and platform selection for production AI infrastructure.
- GPU Fundamentals and Accelerated Computing Principles
- NVIDIA GPU Architecture: H100 Deep-Dive
- GPU Memory Hierarchy and Bandwidth Optimisation
- NVLink and NVSwitch Topology for Multi-GPU Systems
- GPU Sizing Formulas and Capacity Planning
- NVIDIA DGX H100 — Reference System Architecture
- Platform Comparison: DGX, Cisco AI POD, Dell/HPE HGX, and Custom Builds
- Bill of Materials and Total-Cost-of-Ownership Modelling for On-Premises GPU Platforms
GPU Compute Platforms — Bare-Metal and Kubernetes Orchestration
Covers the operational layer that turns GPU hardware into reliable, schedulable infrastructure — from bare-metal tuning through full Kubernetes-based orchestration.
- Bare-Metal GPU Infrastructure: Performance vs. Virtualisation
- GPU Node Tuning: BIOS Configuration, CPU Affinity, and Memory Optimisation
- CUDA Driver Installation and GPU Driver Management
- Kubernetes as the Control Plane for AI Workloads
- NVIDIA GPU Operator: Automated Driver, Runtime, and Plugin Deployment
- GPU Resource Requests, Limits, and QoS in Kubernetes
- MIG Partitioning and Time-Slicing for GPU Sharing
- Hybrid Compute Patterns for Heterogeneous GPU Deployments
High-Performance Networking Fabrics for Distributed AI
Examines the fabric layer that determines whether distributed training scales efficiently, covering RDMA fundamentals, fabric technology comparisons, and topology design.
- Why Traditional Networking Fails for AI Workload Requirements
- RDMA Fundamentals and Advantages
- InfiniBand and Ethernet-Based RDMA (RoCEv2) Comparison
- Lossless Fabric Design: PFC and ECN/DCQCN Tuning
- Collective Communication Patterns: AllReduce, AllGather, Reduce-Scatter, and AllToAll
- RAIL-Optimised Network Topology for Hierarchical GPU Clusters
- NCCL — NVIDIA Collective Communications Library
- NVIDIA Spectrum, Cisco, and Juniper Switch Architectures
GPU Resource Sharing and Multi-Tenant Cluster Management
Addresses how organisations safely and fairly share expensive GPU capacity across multiple teams through partitioning, scheduling, and quota enforcement.
- Multi-Instance GPU (MIG) Partitioning
- Time-Slicing Strategies for GPU Sharing
- Multi-Tenant Cluster Architecture: Namespace Isolation, RBAC, and Network Policies
- GPU Quota Design and Enforcement on Kubernetes
- Run:AI and SLURM Job Scheduling for Distributed Training
- Service Level Objectives (SLOs) and Cluster Capacity Planning
- Monitoring and Metering GPU Utilisation Per Tenant
Storage for AI Workloads
Covers storage architectures purpose-built for AI I/O patterns, ensuring data pipelines can sustain full GPU utilisation across training, checkpointing, and inference.
- Legacy Limitations: Why Traditional NAS/SAN Architectures Bottleneck AI
- NVMe-over-Fabrics (NVMe-oF) Architecture
- Distributed Storage Architectures: VAST Data, Weka.io, and DDN EXAScaler
- NetApp Architecture and Capabilities for AI Workloads
- Object Storage (S3) Integration and Use Cases
- Storage Performance Tuning for Distributed Training
- Kubernetes PersistentVolume Integration with NVMe-oF Backends
- Checkpoint and Artifact Management During Distributed Training
Observability and Operational Excellence
Builds the operational discipline needed to run GPU fleets in production — from telemetry and alerting through structured incident response and upgrade management.
- GPU Cluster Observability Stack — Architecture Overview
- NVIDIA DCGM: Telemetry, Health Diagnostics, and Failure Detection
- Prometheus and Grafana Stack for GPU Metrics
- Alert Configuration and Escalation Policies
- Common Failure Modes in GPU Clusters
- XID Error Reference and Response Guide
- Runbooks and Incident Response Procedures
- Upgrade Strategies: Drivers, CUDA, and Kubernetes
Hybrid Scaling, Compliance, and Cost Optimisation
Closes the course with strategic decision-making: where to place AI workloads, how to remain compliant across jurisdictions, and how to manage GPU infrastructure economics at scale.
- Hybrid Deployment Decision Framework
- On-Premises vs. Cloud vs. Edge — Detailed Comparison
- Multi-Region and Multi-Cloud AI Infrastructure Patterns
- Compliance and Data Residency Enforcement in Hybrid Environments
- Cost Modelling: CapEx vs. OpEx Analysis
- Total Cost of Ownership (TCO) Across Deployment Models
- Cost Optimisation Strategies
- FinOps Governance Framework
Applied Case Studies
The course concludes with analysis of real-world infrastructure decisions, giving participants a framework for evaluating trade-offs in live deployment scenarios:
Case Study 1: On-Premises vs. Cloud GPU Clusters
Evaluating Utilisation, Break-Even Thresholds, and Deployment Fit.
Case Study 2: Storage Architecture for Scale
Matching Storage Design to Real Workload I/O Patterns.
Case Study 3: Multi-Region GDPR Compliance
Designing AI Infrastructure Under Data Residency Constraints.
Ready to begin?
Enroll today for the market introduction price of US$5 (Limited to 50 purchases total).
Register Now — US$5