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Silicon, Power, and Intelligence (Volume-II): Model Compression and Efficient Inference
Paperback
Series: Silicon, Power, and Intelligence - A Hardware-Aware AI Engineering, Book 2
Technology & EngineeringProgramming
Publisher: Independently Published
Published: May 30 2026
Pages: 372
Weight: 1.90
Height: 0.77 Width: 8.50 Depth: 11.00
Language: English
Modern AI models are powerful. Running them efficiently is the real challenge.
As large language models grow to billions and even trillions of parameters, the future of artificial intelligence is no longer defined solely by model capability-it is defined by efficiency. Memory bandwidth, latency, power consumption, context length, and deployment costs have become the new battlegrounds of AI engineering.
In Volume II: Model Compression and Efficient Inference, engineer and researcher Sanzaya Patel explores the technologies that are transforming massive neural networks into practical, deployable systems. From quantization and pruning to knowledge distillation, KV-cache optimization, PagedAttention, FlashAttention, and Mixture-of-Experts architectures, this volume provides a comprehensive engineering roadmap for reducing computational cost while preserving intelligence.
Moving beyond theory, the book reveals how modern AI systems overcome memory bottlenecks, optimize data movement, compress model representations, and maximize performance across edge devices, workstations, and large-scale inference infrastructure.
Inside, you'll discover:
The mathematics and engineering of model quantization
How NF4 and low-bit representations revolutionized LLM deployment
Structural and unstructured pruning techniques
Knowledge distillation and edge fine-tuning strategies
The hidden memory crisis caused by KV caches
How PagedAttention transformed LLM memory management
Why FlashAttention became one of the most important breakthroughs in modern AI systems
The architecture and economics of Mixture-of-Experts models
Practical strategies for building faster, smaller, and more efficient AI systems
Designed for engineers, researchers, architects, students, and AI practitioners, this volume bridges machine learning theory, systems engineering, memory architecture, and deployment optimization into a unified framework for modern inference.
The future of AI belongs not to the largest models, but to the most efficient ones.
Learn how modern intelligence is compressed, accelerated, and deployed at scale.
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