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Sarferaz S: KI in Unternehmen

KI in Unternehmen

Grundlagen, Prozesse, Merkmale und Architektur

The Essential Guide to Launching Your Career in Artificial Intelligence

Break into AI

Artificial Intelligence and the Transformation of Organizations

Algorithmic Management

Running a large language model on hardware you control is no longer exotic, but the gap between a model that loads and a service that holds up under load is where most self-hosting efforts stall. This book closes that gap by building the thing, one working system that grows across all fifteen chapters.You begin with an 8B model on a single card, the kind of hardware you can rent for pennies an hour or already own. From there the book adds what real traffic demands: an inference engine chosen on its merits, a control plane that provisions GPUs and tears them down again, a router and queue that decide what runs next, and cache-aware routing that stops a conversation being re-read from scratch on every turn. Part III moves past what one card can hold, placing a 70B deployment across a pool of GPUs selected on the network between them rather than the sum of their memory, and shopping for that capacity across several providers at once.The final part goes to the frontier. You will serve a 753-billion-parameter sparse model on eight rented H200s, then measure what it costs per session as concurrency climbs, and find the point where a larger batch stops making each session cheaper. That result explains how commercial inference providers price what they sell, and it tells you when self-hosting wins and when it does not.Costs are measured rather than estimated throughout. Every figure in the final part is regenerated from a committed data file, and the book says plainly which numbers are measurements and which are predictions. The complete control plane is available as an open-source companion repository, written in Go, so you can run what you read.You Will:<ul><li>Build a working inference control plane in Go that provisions GPUs across providers, deploys engines onto them, routes and queues requests, and reports what it is doing.</li><li>Learn how inference actually consumes a GPU, covering KV cache growth, quantization formats and their real cost, tensor and expert parallelism, and why a fast interconnect matters more than total memory.</li><li>Measure the economics from real runs, working out per-session cost as batch size and context length grow, and identifying the point where self-hosting stops beating a commercial API.</li></ul>This book is for:Backend, systems and platform engineers who want to run LLMs on infrastructure they control. Also useful to infrastructure and DevOps engineers building internal AI platforms, and to technical founders with data-residency or cost constraints that rule out third-party APIs. No machine learning background is assumed. Readers should be comfortable with a systems language such as Go or Python, with containers, and with HTTP APIs.

Building and Operating Production AI Systems from a Single GPU to Multi-Node Frontier Models

Self-Hosted LLM Inference

Routledge Studies in Accounting

Implications for Practice, Education, and Research

Generative AI and Agentic Systems in Accounting

Risk, Governance and Society

From Data Analytics to Global Risk Governance

Evolution of Anomaly Detection

Routledge Focus on Economics and Finance

AI, Blockchain, and the New Logic of Markets

Aufzeichnungen eines Müllmanns

Dreckskerl

Corporate Governance in the Age of Artificial Intelligence

SpringerBriefs in Applied Sciences and Technology

This book connects the dots between artificial intelligence, energy systems, and computing infrastructure, offering a fresh and systems-based perspective on one of the most important challenges of our time: sustainability in the age of AI. Rather than viewing AI as simply a new consumer of electricity, this book reframes it as a meta-technology —one that can drive system-wide optimization across energy, materials, logistics, and infrastructure.Using a clear systems analysis approach, this book explores how these technologies interact, how data centers are evolving into active components of energy infrastructure, and how public policy, governance, and human decision-making shape outcomes. It goes beyond the usual focus on energy efficiency or technological hype, offering readers a deeper understanding of the causal relationships and strategic implications at play.Essential reading for anyone involved in strategy, innovation, digital transformation, or sustainability, this book provides the tools to make sense of accelerating change and to act responsibly within it. It challenges assumptions, provides a practical framework for future thinking, and makes the case for deliberate, system-wide approaches to digital and ecological futures.

Why the Energy Is Well Spent

Powering the Digital Transformation

Governing the Intelligent Enterprise

Overruled

Hundreds of millions go into ERP, cybersecurity and data platforms. The language that carries all of it into nineteen markets is bought by the word, from whoever quoted lowest. In the AI era that same language has become the training material for everything the company automates.Arthur Wetzel moves language to the other side of the balance. Earlier market entry is revenue. Support tickets that never arrive are margin. An AI system trained on governed content outperforms one trained on whatever was lying around. Language creates all three, and no ledger has ever carried a line for it.This book gives that value a name and a number. The Return on Multilingualism sets the three gains against the investment that produced them. Organizations running LangOps systematically reach a return between 2.8 and 4.1 within three years, the same order of magnitude the DevOps movement documented for its own discipline. The ratio resolves only where one accountable function can see all three at once, which is why so few organizations have ever produced it.From the interpreter on the Bronze Age Euphrates to the focused models now trained on corporate terminology, Wetzel traces how the language industry was built and what it left ungoverned, looks at what Estonia and India did once they treated language as public infrastructure, and sets out LangOps as the framework that turns a recurring purchase into a compounding asset.

KI in Unternehmen

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