Inference optimization engineering quantization par team chatvariety (4 résultats)
Langue : anglais
Edité par Independently published, 2026
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Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 13,59
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New. Print on Demand.
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Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 13,59
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New. Print on Demand.
Langue : anglais
Edité par Independently Published Jun 2026, 2026
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Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 13,00
EUR 60,71 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Taschenbuch. Etat : Neu. Neuware - Slash LLM Deployment Costs and LatencyDeploying 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-ban…dwidth 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.
Langue : anglais
Edité par Independently Published Jun 2026, 2026
- Couverture souple
Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 13,00
EUR 60,63 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Taschenbuch. Etat : Neu. 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 wi…th 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.
