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Könyv AI Inference Optimization Engineering ChatVariety Team

AI Inference Optimization Engineering

Quantization, Speculative Decoding, and Hardware-Specific LLM Deployment

Szerző: ChatVariety Team
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Independently published
Elérhetőség: Várható készletfeltöltés
Küldés 07. 06. 2026
3 923 Ft
Slash LLM Deployment Costs and LatencyDeploying Large Language Models (LLMs) in production is a mass...

Információk a könyvről

Szerző
ChatVariety Team
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2026
oldal
96
EAN
9798199720021
Enbook ID
52770465
Kiadó
Independently published
Súly
142
Méretek
152 x 229 x 5

Teljes leírás

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.