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{
"item_title" : "AI Inference Optimization Engineering",
"item_author" : [" Chatvariety Team "],
"item_description" : "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.",
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AI Inference Optimization Engineering : Quantization, Speculative Decoding, and Hardware-Specific LLM Deployment
Overview
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.
This item is Non-Returnable
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Details
- ISBN-13: 9798199720021
- ISBN-10: 9798199720021
- Publisher: Independently Published
- Publish Date: June 2026
- Dimensions: 9 x 6 x 0.2 inches
- Shipping Weight: 0.31 pounds
- Page Count: 96
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