AI Inference Optimization Engineering (Paperback)
Language: English
Published by Independently Published, 2026
- Softcover
- New
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
AbeBooks seller since June 29, 2022
Condition: New
£ 13.99
Quantity: 1 available
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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-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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
Seller Inventory # 9798199720021
- Title
- AI Inference Optimization Engineering (Paperback)
- Author
- Chatvariety Team
- Publisher
- Independently Published
- Publication year
- 2026
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 13
- 9798199720021
- Series
- Book 6 of 21: Production AI Engineering Series
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.
"Synopsis" may belong to another edition of this title.
CitiRetail
Stevenage, United Kingdom
AbeBooks seller since June 29, 2022
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