AI Inference Optimization Engineering : Quantization, Speculative Decoding, and Hardware-Specific LLM Deployment

Chatvariety Team

ISBN 13: 9798199720021

Edité par Independently Published Jun 2026, 2026

A propos de cet article

Neuware.

N° de réf. du vendeur 9798199720021

Détails bibliographiques

• Titre: AI Inference Optimization Engineering : Quantization, Speculative Decoding, and Hardware-Specific LLM Deployment
• Éditeur: Independently Published Jun 2026
• Date de parution: 2026
• Langue: anglais
• ISBN à 13 chiffres: 9798199720021
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 229 L x 152 H x 5 P (en millimètres) 229 millimètres de largeur par 152 millimètres de hauteur par 5 millimètres de profondeur
• Poids: 142 grammes

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Synopsis

Slash LLM Deployment Costs and Latency

Deploying Large Language Models (LLMs) in production is a massive economic and engineering hurdle. AI Inference Optimization Engineering is your comprehensive, hands-on guide to mastering the full stack of modern LLM optimization techniques. From memory-bandwidth solutions to hardware-specific compilation, this book bridges the gap between research-level models and enterprise-grade execution.

What you will master inside this book:

• Hardware-Aware Optimization: Dive deep into KV cache mechanics, autoregressive decoding, and GPU memory hierarchies to eliminate latency bottlenecks.
• State-of-the-Art Quantization: Apply GPTQ, AWQ, and GGUF compression algorithms to scale down massive neural networks without sacrificing model accuracy.
• Advanced Acceleration Methods: Implement speculative decoding with draft models (like Medusa and Eagle), PagedAttention, and FlashAttention to boost throughput by 2-3x.
• Production-Grade Serving: Build ultra-low-latency deployment infrastructures using vLLM, Triton Inference Server, and continuous batching.
• Cross-Platform Deployment: Optimize models for specific target hardware, including NVIDIA H100 (TensorRT-LLM), Apple Silicon (llama.cpp/Metal), and Qualcomm mobile/edge accelerators.

Whether you are an ML infrastructure engineer, an AI platform architect, or a technical leader looking to scale LLMs cost-effectively, this book provides the production-ready code, equations, and architectural patterns you need to build hyper-efficient AI pipelines.