LLM Inference Engineering : Quantization, KV-Cache Optimization, and High-Throughput Serving: A Production Engineer's Guide to INT4/INT8 Quantization, vLLM, TGI, Speculative Decoding, and Cost Optimization

Chatvariety Team

ISBN 13: 9798180985187

Edité par Independently Published Jun 2026, 2026

A propos de cet article

Neuware - Master the Art of Low-Latency, High-Throughput LLM ServingIn 2026, the defining challenge of production AI is no longer training-it is cost-effective inference. LLM Inference Engineering is the definitive production guide for software engineers, ML developers, and DevOps professionals tasked with deploying large language models at scale without breaking the bank.This hands-on manual strips away the theoretical academic jargon and delivers practical, production-ready strategies to cut your GPU and cloud serving costs by 50% to 70% while maintaining absolute response quality.What You Will Master: - Advanced Quantization: Hands-on implementation of INT4/INT8 quantization using AWQ, GPTQ, and GGUF algorithms without destroying model accuracy.- High-Throughput Architectures: Deep dives into PagedAttention, continuous batching, and GPU memory management to maximize hardware utilization.- Serving Frameworks: Configuration recipes and production tuning guidelines for vLLM, TGI (Text Generation Inference), and llama.cpp.- Speed Optimization: Implement speculative decoding to achieve 2x to 4x latency reduction with mathematically guaranteed quality.- Scaling to 70B+ Models: Configure multi-GPU setups using tensor parallelism to distribute memory footprints efficiently.- Rigorous Benchmarking: Establish robust metrics for latency, cost-per-token, and throughput to justify infrastructure decisions.Written specifically for practicing engineers, this guide assumes familiarity with Python and basic PyTorch. Inside, you will find real-world deployment examples, benchmarking code, and architectural breakdowns that bridge the gap between model training and highly scalable production deployments. Equip yourself with the skills to architect the next generation of AI infrastructure. Stop wasting expensive GPU cycles-optimize your inference pipeline today.

N° de réf. du vendeur 9798180985187

Détails bibliographiques

• Titre: LLM Inference Engineering : Quantization, KV-Cache Optimization, and High-Throughput Serving: A Production Engineer's Guide to INT4/INT8 Quantization, vLLM, TGI, Speculative Decoding, and Cost Optimization
• Éditeur: Independently Published Jun 2026
• Date de parution: 2026
• Langue: anglais
• ISBN à 13 chiffres: 9798180985187
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 229 L x 152 H x 4 P (en millimètres) 229 millimètres de largeur par 152 millimètres de hauteur par 4 millimètres de profondeur
• Poids: 126 grammes

A propos de ce titre

Synopsis

Master the Art of Low-Latency, High-Throughput LLM Serving

In 2026, the defining challenge of production AI is no longer training—it is cost-effective inference. LLM Inference Engineering is the definitive production guide for software engineers, ML developers, and DevOps professionals tasked with deploying large language models at scale without breaking the bank.

This hands-on manual strips away the theoretical academic jargon and delivers practical, production-ready strategies to cut your GPU and cloud serving costs by 50% to 70% while maintaining absolute response quality.

What You Will Master:

• Advanced Quantization: Hands-on implementation of INT4/INT8 quantization using AWQ, GPTQ, and GGUF algorithms without destroying model accuracy.
• High-Throughput Architectures: Deep dives into PagedAttention, continuous batching, and GPU memory management to maximize hardware utilization.
• Serving Frameworks: Configuration recipes and production tuning guidelines for vLLM, TGI (Text Generation Inference), and llama.cpp.
• Speed Optimization: Implement speculative decoding to achieve 2x to 4x latency reduction with mathematically guaranteed quality.
• Scaling to 70B+ Models: Configure multi-GPU setups using tensor parallelism to distribute memory footprints efficiently.
• Rigorous Benchmarking: Establish robust metrics for latency, cost-per-token, and throughput to justify infrastructure decisions.

Written specifically for practicing engineers, this guide assumes familiarity with Python and basic PyTorch. Inside, you will find real-world deployment examples, benchmarking code, and architectural breakdowns that bridge the gap between model training and highly scalable production deployments. Equip yourself with the skills to architect the next generation of AI infrastructure. Stop wasting expensive GPU cycles—optimize your inference pipeline today.