Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
GPU Cluster Architecture and Operations: Engineering Scalable AI Infrastructure with High-Speed Networking, Kubernetes, Distributed Training, Telemetr

GPU Cluster Architecture and Operations: Engineering Scalable AI Infrastructure with High-Speed Networking, Kubernetes, Distributed Training, Telemetr

Richards, Pete

Paperback

General ComputersComputer SecuritySystem Administration

ISBN13: 9798140280062
Publisher: Independently Published
Published: Nov 5 2026
Pages: 324
Weight: 1.24
Height: 0.68 Width: 7.00 Depth: 10.00
Language: English
Building a GPU cluster is one challenge. Operating it reliably at scale is another.Modern AI infrastructure demands more than powerful accelerators and high-speed connections. Production GPU clusters must support distributed workloads, maintain predictable performance, accommodate changing resource demands, and recover from hardware, network, storage, and software failures without unnecessary disruption.For AI infrastructure engineers, platform teams, and site reliability engineers, these requirements introduce complex architectural and operational decisions that extend far beyond GPU provisioning.GPU Cluster Architecture and Operations by Pete Richards is an advanced technical reference to designing, deploying, monitoring, securing, optimizing, and maintaining scalable GPU infrastructure for demanding artificial intelligence workloads.Combining distributed systems principles with practical production engineering, this book explains how GPU computing, high-speed networking, Kubernetes orchestration, distributed training, telemetry, and fault tolerance operate together within modern AI platforms.What You Will Learn
  • GPU Cluster Architecture and Capacity Planning: Design production GPU platforms, evaluate workload requirements, manage accelerator fleets, forecast resource demand, and plan infrastructure expansion.
  • Infrastructure Provisioning and Lifecycle Management: Standardize bare-metal GPU nodes, manage drivers and firmware, maintain software compatibility, control configuration drift, and execute infrastructure upgrades.
  • High-Speed Networking and RDMA Operations: Understand InfiniBand, RoCE, Ethernet fabrics, network congestion, communication bottlenecks, connectivity failures, and recovery procedures.
  • Distributed Storage and Data Pipelines: Evaluate parallel file systems, object storage, dataset staging, checkpoint management, storage performance, and I/O reliability.
  • Kubernetes GPU Resource Management: Operate GPU-enabled Kubernetes environments, configure accelerator discovery, manage scheduling policies, support multi-tenant workloads, and maintain resource isolation.
  • Distributed Training and Inference Operations: Manage distributed AI workloads, investigate communication delays, diagnose stalled training jobs, maintain inference availability, and implement recovery strategies.
  • GPU Telemetry and Production Observability: Monitor accelerator health using NVIDIA DCGM, Prometheus, and Grafana. Correlate GPU metrics, application behavior, network performance, and infrastructure events to identify operational problems.
  • Reliability Engineering and Fault Tolerance: Investigate GPU hardware faults, manage node failures, develop automated recovery procedures, conduct resilience testing, and establish disaster recovery practices.
  • Security, Performance, and Cost Optimization: Implement access controls, strengthen workload isolation, evaluate GPU utilization, optimize scheduling efficiency, and assess infrastructure operating costs.
  • Production Operations and Incident Response: Establish service-level objectives, create operational runbooks, manage infrastructure changes, conduct health reviews, and evaluate production readiness.
For engineers responsible for keeping AI infrastructure available, efficient, observable, and resilient, this book provides a detailed technical foundation for operating GPU clusters with greater confidence and control.

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