AI Inference Engineering: Foundations of LLM Inference, Model Serving, GPU Computing, Performance, and Cost Optimization (The AI Inference Engineering Series Book 1)

Artificial intelligence has moved beyond the challenge of training capable models. The next major engineering challenge is inference : determining how models can execute efficiently, serve real workloads, meet latency requirements, utilize hardware effectively, and operate at sustainable cost.

AI Inference Engineering provides a practical systems-oriented foundation for understanding modern AI inference, with particular emphasis on large language models, GPU computing, model serving, performance, and infrastructure economics.

The book explains what happens between an inference request and a model-generated response. Readers explore the computational architecture behind neural-network inference, CPU and GPU execution, transformer workloads, tokenization, prefill and decode, autoregressive generation, model memory, activations, KV caches, batching, latency, throughput, and resource utilization.

Rather than treating inference as simply the final stage of machine learning, this book presents it as a distinct engineering discipline governed by compute, memory, bandwidth, workload characteristics, architecture, performance, and cost .

Inside the Book, You Will Learn How To: Understand the difference between model training and inference. Analyze modern AI inference workloads and computational requirements. Understand CPU and GPU architectures for machine-learning workloads. Trace the execution of transformer-based LLM inference. Understand tokenization, prefill, decode, and autoregressive generation. Analyze model weights, activations, runtime memory, and KV-cache requirements. Understand latency, throughput, concurrency, and time-to-first-token. Evaluate static, dynamic, and continuous batching. Understand model-serving architectures and inference APIs. Design reproducible inference benchmarks. Measure latency, throughput, and hardware utilization. Identify compute, memory, and scheduling bottlenecks. Estimate infrastructure requirements and inference costs. Analyze cost per request and cost per generated token. Develop a systematic approach to inference performance engineering. The book is designed for AI/ML engineers, software developers, MLOps practitioners, DevOps and platform engineers, cloud engineers, data scientists, technical students, and professionals transitioning into AI infrastructure engineering . Familiarity with programming, basic machine learning, and fundamental computer-system concepts will be useful, but the material is structured to develop the required systems perspective progressively.

By the end of the book, readers will have a strong systems-level understanding of how AI inference works and how computational resources, workload characteristics, and serving architecture influence performance and cost.

AI Inference Engineering is the foundational volume of The AI Inference Engineering Series , establishing the knowledge required to progress into advanced inference optimization, production LLM serving, and large-scale distributed AI infrastructure.