9798199720021 - ai inference optimization engineering: quantization, speculative decoding, and hardware-specific llm deployment par team, chatvariety (4 résultats)
Langue : anglais
Edité par Independently published, 2026
Série : Production AI Engineering Series, Livre 6 sur 8. Livre 6 sur 8 - Production AI Engineering Series
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Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-UniPBShop.store UK
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EUR 13,50
EUR 3,87 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : Plus de 20 disponibles
PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.
Langue : anglais
Edité par Independently published, 2026
Série : Production AI Engineering Series, Livre 6 sur 8. Livre 6 sur 8 - Production AI Engineering Series
- Couverture souple
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Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
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EUR 13,51
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Etat : New. Print on Demand.
Langue : anglais
Edité par Independently Published Jun 2026, 2026
Série : Production AI Engineering Series, Livre 6 sur 8. Livre 6 sur 8 - Production AI Engineering Series
- Couverture souple
Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
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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, 2026
Série : Production AI Engineering Series, Livre 6 sur 8. Livre 6 sur 8 - Production AI Engineering Series
- Couverture souple
- impression à la demande
Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 16,94
EUR 43,51 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Paperback. Etat : new. Paperback. 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-band…width 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
