LLM Production Deployment Practical Handbook: A Complete Guide from Theory to Real-World Testing

The LLM Production Deployment Practical Handbook: A Complete Guide from Theory to Real-World Testing is an open-source practical guide focusing on the deployment of large language models in production environments. It aims to help AI engineers solve core challenges in efficient and stable model deployment. The handbook covers theoretical foundations, paper interpretations, engine source code analysis, and real hardware benchmark tests, providing systematic knowledge of LLM service architecture and balancing production-level requirements such as latency, throughput, cost, and scalability.

This handbook is written by practitioners and positioned as a "practical guide with perspectives", distinguishing itself from common resource link collections (e.g., awesome lists). Each technical topic is written from scratch, accompanied by runnable code and reproducible benchmark tests to ensure readers understand the principles and can verify results in real environments. The content includes theoretical analysis, paper notes, source code-level analysis, and real hardware testing, forming a complete chain from theory to real-world testing.

The handbook is structured in an organized manner. Each topic includes a README overview, theoretical analysis, paper notes, engine implementation analysis, experimental code, benchmark data, decision-making guidelines, and reference resources. It plans to cover 85 technical topics across 10 core areas: basic theory, inference optimization techniques, parallel and distributed strategies, inference engine analysis, service orchestration and infrastructure, gateway and security protection, LoRA and adapter services, observability and evaluation, cost optimization and hardware selection, and cutting-edge trends.

The project is in the early stage. The first topic, "Anatomy of LLM Inference", is under writing, while the remaining 84 topics are planned and pending completion. An iterative strategy of "complete one, release one" is adopted to ensure content depth and quality. Developers are welcome to contribute theoretical supplements, experimental reproductions, code, or bug fixes. It is recommended to follow project updates or start learning and verifying from completed topics.

The handbook is suitable for multiple types of readers: LLM service architects (for technology selection), inference engine developers (for reference of implementation details), MLOps engineers (for deployment monitoring and cost optimization), AI researchers (for industrial demand solutions), and technical decision-makers (for infrastructure investment strategies).

The LLM Serving Handbook is a new model of knowledge precipitation in the LLM engineering field, characterized by in-depth original content and experiment-driven approaches, providing important references for production deployment teams. As the 85 topics are gradually improved, it is expected to become an authoritative knowledge base in the LLM service field.