LLM System Interview Front Matter Title Page Contents Preface How to Use This Book For Candidates Preparing for Interviews For Engineers Building LLM Systems Notation and Symbols Acknowledgments About the Author Chapters Part I — Overview and Interview Landscape The LLM Systems Interview How to Approach an LLM System Design Question Part II — Foundations: Transformer Architecture and Training Signals Transformer Architecture Interview Essentials Hyperparameters You Will Be Asked to Justify Stability Tricks in Large-Scale Training Part III — Mixture of Experts and Sparse Architectures Why MoE Is Now Standard in Frontier Systems MoE Training and Systems Considerations Part IV — GPUs, Kernels, and Single-Device Performance GPU Architecture for LLM Engineers Making a Single GPU Go Fast Writing and Benchmarking Custom Kernels Part V — Distributed Training: Parallelism at Scale Multi-GPU and Multi-Node Fundamentals Parallelism Strategies Part VI — Scaling Laws and Training Economics Predictable Scaling for Interview Answers Chinchilla and Beyond Part VII — Inference Systems The Inference Workload Reducing the KV Cache Going Beyond the Transformer for Inference Speculative Decoding and Serving Optimizations Compression Techniques for Deployment Part VIII — Data: The Real Differentiator Pre-Training Data Pipelines Data Filtering and Deduplication Algorithms Mid-Training and Post-Training Data Part IX — Evaluation Designing an Evaluation Benchmarks You Must Know Validity, Contamination, and Real-World Use Part X — Alignment and Post-Training Supervised Fine-Tuning Preference-Based Alignment (RLHF) Reinforcement Learning from Verifiable Rewards Part XI — End-to-End System Design Drills Building a Production LLM Serving Stack Designing a Pre-Training Run from Scratch Designing a Fine-Tuning and Alignment Pipeline Appendices Napkin Math for LLM Interviews Common Interview Questions Checklists for System Design Answers Further Reading Navigation Reading Routes Source Index