AI Inference Optimization Engineering: Quantization, Speculative Decoding, and Hardware-Specific LLM Deployment (Production AI Engineering Series) - Tapa blanda

Sinopsis

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.

• 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.

"Sinopsis" puede pertenecer a otra edición de este libro.