SKU/Artículo: AMZ-B0GFCK7DZK
GPU Passthrough & AI Workloads on Hypervisors : Run AI, Media, and High-Performance Compute Workloads on Proxmox & KVM Using GPU Passthrough and vGPU
Format:
Kindle
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En stock
En stock
Peso con empaque:
0.76 kg
0.76 kg
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Sí
Sí
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Nuevo
Nuevo
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Amazon
Amazon
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USA
USA
Sobre este producto
- Modern AI, media, and compute workloads demand GPUs—but running them reliably on virtualized infrastructure is where most guides fall apart. Misleading shortcuts, fragile hacks, and undocumented edge cases turn GPU passthrough into a trial-and-error exercise that rarely survives real load, reboots, or upgrades.GPU Passthrough & AI Workloads on Hypervisors is a hands-on, systems-level guide to doing this correctly.This book teaches you how to design, build, and operate production-grade GPU-accelerated workloads on Proxmox VE and KVM using GPU passthrough and supported vGPU paths—with performance, stability, observability, and recovery built in from day one.You won’t find theory without execution here. Every chapter is practical, lab-driven, and grounded in real constraints faced by homelabs, on-prem teams, and small to mid-scale platforms.What you’ll learnHow GPU passthrough and supported vGPU actually work under the hood (VFIO, IOMMU, firmware, kernel behavior)How to validate hardware before you waste time configuring itHow to build passthrough VMs that boot consistently and perform like bare metalHow to share GPUs safely using supported multi-tenant paths—without instabilityHow to run real workloads that matter:GPU-backed AI inference services and APIsHardware-accelerated media transcoding pipelinesLightweight HPC and compute workloads with repeatable benchmarksHow to design for performance: CPU topology, NUMA alignment, storage and network pathsHow to operate GPU nodes professionally with monitoring, alerting, fault testing, restores, and safe upgradesWhat makes this book differentPurely practical: no fluff, no marketing diagrams, no unsupported tricksModern and up-to-date: aligned with 2025+ Proxmox, KVM, GPU drivers, and AI runtimesOperationally complete: observability, acceptance tests, rollback plans, and recovery drills are first-class topicsReproducible by design: Ansible-first workflows, pinned versions, and validation templates throughoutEnd-to-end: a full-stack capstone project that proves everything works together under loadWho this book is forHomelab builders running serious workloads, not experimentsInfrastructure and platform engineers deploying GPU nodes on-prem or at the edgeDevOps and SRE professionals responsible for stability, upgrades, and recoveryAI practitioners who need predictable, low-latency inference on private infrastructureAnyone migrating GPU workloads away from fragile, cloud-only assumptionsWhat this book is notNot a CUDA programming manualNot a vendor marketing guideNot a collection of risky hacks or license-bypassing shortcutsInstead, it is a discipline-driven guide to making GPU virtualization boring, reliable, and defensible.By the end of this book, you won’t just “have GPU passthrough working.” You will have a validated GPU compute platform—with measurable baselines, monitored workloads, tested recovery paths, and the confidence to upgrade, scale, and troubleshoot without guesswork.If you want your GPUs to work for you, not against you, this book is written for exactly that purpose.
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AR$24.041
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AR$16.575
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