Elliot D Grayson (10 results)
Language: English
Published by Independently published, 2026
Series: Book 1 of 2 - The Economics of AI Infrastructure for AI Engineering and Large Language Models
- Softcover
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Language: English
Published by Independently published, 2026
Series: Book 1 of 2 - The Economics of AI Infrastructure for AI Engineering and Large Language Models
- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
Language: English
Published by Mercer Education Press, 2026
Series: Book 2 of 2 - The Economics of AI Infrastructure for AI Engineering and Large Language Models
- Softcover
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
Language: English
Published by Independently published, 2026
Series: Book 2 of 2 - The Economics of AI Infrastructure for AI Engineering and Large Language Models
- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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Language: English
Published by Independently Published Jun 2026, 2026
Series: Book 1 of 2 - The Economics of AI Infrastructure for AI Engineering and Large Language Models
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Neuware - Artificial intelligence is transforming industries, reshaping software development, and redefining the future of computing. But behind every AI-generated response lies a question few people ask: Why is AI so expensive Every token generated by a large language model consumes compute resource…s. Every inference request requires memory, networking, energy, and specialized hardware. Every AI application depends on a vast infrastructure ecosystem working behind the scenes to transform computation into intelligence.This book explores the hidden economics of modern AI systems.Rather than focusing on prompt engineering or machine learning theory, The Economics of AI Infrastructure for AI Engineering and Large Language Models examines the physical, operational, and financial foundations that power today's AI platforms. It explains why training large language models costs millions of dollars, why inference is becoming one of the industry's largest operational expenses, and how modern organizations optimize infrastructure to deliver intelligence at scale.Written for AI engineers, software architects, technology leaders, infrastructure professionals, researchers, and serious students of artificial intelligence, this volume provides a systems-level understanding of the technologies that make large-scale AI possible.Inside this book, you'll learn: - Why AI systems are fundamentally infrastructure systems- The economics of training large language models- How inference workloads drive operational costs- Why GPUs and AI accelerators dominate modern AI computing- The role of memory architecture in AI performance- How networking impacts scalability and throughput- Why AI datacenters are becoming the factories of the intelligence economy- How quantization and model optimization improve profitability- The architecture of modern AI serving platforms- The economics of multi-tenant AI systems- The tradeoffs between open models and closed AI ecosystemsThroughout the book, complex technical concepts are explained through the lens of real-world infrastructure, operational tradeoffs, and business economics. Readers will gain a deeper understanding of how compute, memory, networking, storage, energy, and software systems interact to support modern AI applications.Whether you're designing AI platforms, evaluating infrastructure investments, building large language model applications, or simply seeking to understand the economics behind the AI revolution, this book provides the foundation needed to think like an AI infrastructure engineer.Because the future of artificial intelligence will not be defined solely by smarter models.It will be defined by the infrastructure that makes those models possible.Volume 1 of a 2-volume series on AI Infrastructure, AI Engineering, and Large Language Models.
Language: English
Published by Independently published, 2026
Series: Book 1 of 2 - The Economics of AI Infrastructure for AI Engineering and Large Language Models
- Softcover
- Print on Demand
Seller: California Books, Miami, FL, U.S.A.California Books
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Language: English
Published by Independently published, 2026
Series: Book 2 of 2 - The Economics of AI Infrastructure for AI Engineering and Large Language Models
- Softcover
- Print on Demand
Seller: California Books, Miami, FL, U.S.A.California Books
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The Economics of AI Infrastructure for AI Engineering and Large Language Models Volume 1 (Paperback)
Language: English
Published by Independently Published, 2026
Series: Book 1 of 2 - The Economics of AI Infrastructure for AI Engineering and Large Language Models
- Softcover
- Print on Demand
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: new. Paperback. Artificial intelligence is transforming industries, reshaping software development, and redefining the future of computing. But behind every AI-generated response lies a question few people ask: Why is AI so expensive?Every token generated by a large language model consumes compute resources…. Every inference request requires memory, networking, energy, and specialized hardware. Every AI application depends on a vast infrastructure ecosystem working behind the scenes to transform computation into intelligence.This book explores the hidden economics of modern AI systems.Rather than focusing on prompt engineering or machine learning theory, The Economics of AI Infrastructure for AI Engineering and Large Language Models examines the physical, operational, and financial foundations that power today's AI platforms. It explains why training large language models costs millions of dollars, why inference is becoming one of the industry's largest operational expenses, and how modern organizations optimize infrastructure to deliver intelligence at scale.Written for AI engineers, software architects, technology leaders, infrastructure professionals, researchers, and serious students of artificial intelligence, this volume provides a systems-level understanding of the technologies that make large-scale AI possible.Inside this book, you'll learn: Why AI systems are fundamentally infrastructure systemsThe economics of training large language modelsHow inference workloads drive operational costsWhy GPUs and AI accelerators dominate modern AI computingThe role of memory architecture in AI performanceHow networking impacts scalability and throughputWhy AI datacenters are becoming the factories of the intelligence economyHow quantization and model optimization improve profitabilityThe architecture of modern AI serving platformsThe economics of multi-tenant AI systemsThe tradeoffs between open models and closed AI ecosystemsThroughout the book, complex technical concepts are explained through the lens of real-world infrastructure, operational tradeoffs, and business economics. Readers will gain a deeper understanding of how compute, memory, networking, storage, energy, and software systems interact to support modern AI applications.Whether you're designing AI platforms, evaluating infrastructure investments, building large language model applications, or simply seeking to understand the economics behind the AI revolution, this book provides the foundation needed to think like an AI infrastructure engineer.Because the future of artificial intelligence will not be defined solely by smarter models.It will be defined by the infrastructure that makes those models possible.Volume 1 of a 2-volume series on AI Infrastructure, AI Engineering, and Large Language Models. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
The Economics of AI Infrastructure for AI Engineering and Large Language Models Volume 1 (Paperback)
Language: English
Published by Independently Published, 2026
Series: Book 1 of 2 - The Economics of AI Infrastructure for AI Engineering and Large Language Models
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. Condition: new. Paperback. Artificial intelligence is transforming industries, reshaping software development, and redefining the future of computing. But behind every AI-generated response lies a question few people ask: Why is AI so expensive?Every token generated by a large language model consumes compute resources…. Every inference request requires memory, networking, energy, and specialized hardware. Every AI application depends on a vast infrastructure ecosystem working behind the scenes to transform computation into intelligence.This book explores the hidden economics of modern AI systems.Rather than focusing on prompt engineering or machine learning theory, The Economics of AI Infrastructure for AI Engineering and Large Language Models examines the physical, operational, and financial foundations that power today's AI platforms. It explains why training large language models costs millions of dollars, why inference is becoming one of the industry's largest operational expenses, and how modern organizations optimize infrastructure to deliver intelligence at scale.Written for AI engineers, software architects, technology leaders, infrastructure professionals, researchers, and serious students of artificial intelligence, this volume provides a systems-level understanding of the technologies that make large-scale AI possible.Inside this book, you'll learn: Why AI systems are fundamentally infrastructure systemsThe economics of training large language modelsHow inference workloads drive operational costsWhy GPUs and AI accelerators dominate modern AI computingThe role of memory architecture in AI performanceHow networking impacts scalability and throughputWhy AI datacenters are becoming the factories of the intelligence economyHow quantization and model optimization improve profitabilityThe architecture of modern AI serving platformsThe economics of multi-tenant AI systemsThe tradeoffs between open models and closed AI ecosystemsThroughout the book, complex technical concepts are explained through the lens of real-world infrastructure, operational tradeoffs, and business economics. Readers will gain a deeper understanding of how compute, memory, networking, storage, energy, and software systems interact to support modern AI applications.Whether you're designing AI platforms, evaluating infrastructure investments, building large language model applications, or simply seeking to understand the economics behind the AI revolution, this book provides the foundation needed to think like an AI infrastructure engineer.Because the future of artificial intelligence will not be defined solely by smarter models.It will be defined by the infrastructure that makes those models possible.Volume 1 of a 2-volume series on AI Infrastructure, AI Engineering, and Large Language Models. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
The Economics of AI Infrastructure for AI Engineering and Large Language Models Volume 2 (Paperback)
Language: English
Published by Independently Published, 2026
Series: Book 2 of 2 - The Economics of AI Infrastructure for AI Engineering and Large Language Models
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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US$ 48.02
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Paperback. Condition: new. Paperback. Artificial intelligence is no longer an experimental technology.Large language models are now embedded in enterprise software, customer-facing applications, business workflows, development platforms, research environments, and increasingly autonomous systems. As organizations move from AI ex…perimentation to large-scale deployment, a new challenge emerges: how do you operate intelligence reliably, securely, efficiently, and at scale?This book explores the operational side of modern AI.While Volume 1 examined the economics and infrastructure foundations behind AI systems, Volume 2 focuses on the governance, security, optimization, and operational disciplines required to transform AI from a technical capability into a dependable business platform.Modern AI systems are far more than models. They are complex operational ecosystems that include retrieval systems, governance frameworks, security controls, distributed infrastructure, observability platforms, energy systems, and increasingly autonomous workflows. Understanding these systems has become essential for anyone responsible for building, managing, or scaling AI in production environments.Inside this volume, you will learn: - How organizations govern AI systems in high-scale environments- Why auditability, traceability, and human oversight are becoming critical requirements- The operational mathematics behind token throughput and inference optimization- How retrieval-augmented generation (RAG) changes infrastructure economics- The hidden costs of embeddings, vector databases, and retrieval systems- Why energy has become a strategic factor in AI infrastructure planning- How modern organizations approach AI security and operational risk- The role of sovereign compute in national AI strategies- How edge AI is reshaping inference architecture- Why custom silicon is transforming the economics of AI computing- How autonomous infrastructure optimization is changing operations- Why AI operations and cloud operations are converging into a new engineering disciplineRather than focusing on theory alone, this book examines the real-world systems, tradeoffs, and operational decisions that determine whether AI deployments succeed or fail.This book is written for: - AI Engineers and Machine Learning Engineers- Software Architects and Platform Engineers- Cloud and Infrastructure Engineers- Technical Product Leaders- CTOs, Engineering Directors, and Technology Executives- AI Researchers seeking operational perspective- Enterprise Decision-Makers evaluating AI strategy- Students and professionals preparing for the next generation of AI systemsWhether you are designing AI platforms, deploying large language models, evaluating infrastructure investments, building retrieval systems, or leading AI initiatives within your organization, this book provides the operational knowledge needed to understand how intelligence systems function at scale.The future of artificial intelligence will not be determined solely by larger models or better benchmarks. It will be determined by how effectively organizations govern, secure, optimize, and scale intelligent systems in real-world environments.This book provides the framework, concepts, and operational perspective required to understand that future.If Volume 1 explained why AI is expensive, Volume 2 explains how the world's most important AI systems can be operated successfully.A must-read for engineers, architects, technology leaders, and serious practitioners seeking to understand the operational foundations of the intelligence economy. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
