LLM INFERENCE ENGINEERING: Optimizing Large Language Models on NVIDIA GPUs

A comprehensive technical guide to building and deploying production LLM inference systems. Covers transformer architecture through AI factory design, with deep dives into NVIDIA GPU architectures (H100, B200, Grace Hopper), continuous batching, model optimization, and rack-scale deployment. Designed for Solution Architect candidates and AI infrastructure engineers.Key Topics: Transformer architecture • KV caching • FlashAttention • Continuous batching • GPU memory hierarchy • vLLM & TensorRT-LLM • Grace Hopper & Blackwell • Model parallelism • Production serving • AI datacenter designUnique Features:100+ interview questions with detailed answersReal-world TCO analysis and cost optimizationGPU-specific optimization techniques throughout17 comprehensive chapters covering fundamentals to advanced deploymentProduction-ready patterns and best practicesTarget Audience: AI infrastructure engineers, Solution Architect candidates, ML platform engineers and technical architects building LLM systems at scale. Read more