rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: AI data center network design and technologies
  name:
    name part: Subramaniam, Mahesh
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Styszynski, Michal
    role:
      role term: author.
  name:
    name part: Tambakuwala, Himanshu
    role:
      role term: author.
  name:
    name part: Safari, an O'Reilly Media Company
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: nju
    date issued: 2026
    copyright date: 2026
    edition: [First edition].
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (384 pages)
  abstract: Artificial intelligence is redefining the scale, architecture, and performance expectations of modern data centers. Training large ML models demand infrastructure capable of moving massive data sets through highly parallel, compute-intensive environmentswhere traditional data center designs simply cant keep up. AI Data Center Network Design and Technologies is the first comprehensive, vendor-agnostic guide to the design principles, architectures, and technologies that power AI training and inference clusters. Written by leading experts in AI Data center design, this book helps engineers, architects, and technology leaders understand how to design and scale networks purpose-built for the AI era. INSIDE, YOULL LEARN HOW TO Architect scalable, high-radix network fabrics to support xPU (GPE, TPU)-based AI clusters Integrate lossless Ethernet/IP fabrics for high-throughput, low-latency data movement Align network design with AI/ML workload characteristics and server architectures Address challenges in cooling, power, and interconnect design for AI-scale computing Evaluate emerging technologies from the Ultra Ethernet Consortium (UEC) and their affect on future AI data centers Apply best practices for deployment, validation, and performance measurement in AI/ML environments With broad coverage of both foundational concepts and emerging innovations, this book bridges the gap between network engineering and AI infrastructure design. It empowers readers to understand not only how AI data centers workbut why they must evolve.
  note: Mahesh Subramaniam, Michal Styszynski, Himanshu Tambakuwala.
  subject:
    topic: Data centers
    topic: Design and construction
  subject:
    topic: Information technology
    topic: Technological innovations
  subject:
    topic: Technologies de l'information et de la communication
  classification: TJ163.5.D38
  classification: 621.39
  identifier: 9780135436288
  identifier: 9780135436370 O'Reilly Media
  identifier: https://go.oreilly.com/TAMU/library/view/-/9780135436370/
  record info:
    record content source: ORMDA
    record creation date: 260127
    record change date: 20260219192248.7
    record identifier: in00005691931
    language of cataloging:
      language term: eng