LLM Inference Systems: A Systematic Guide to Large Model Inference Infrastructure

This open-source textbook focuses on LLM inference systems, systematically covering full-stack knowledge from model deployment and service architecture to performance optimization. It fills the gap in systematic learning resources for this field, providing engineers and researchers with a complete path to deeply understand LLM inference and helping them master the design principles of inference systems—a core competency for AI engineers.

The inference phase of large models is a continuous operational cost that directly affects product availability and cost (an efficient system can serve ten times more users or reduce latency by one-tenth); LLM inference design is complex (involving multiple dimensions such as autoregressive generation, KV caching, and distributed deployment); existing resources are scattered across papers, blogs, etc., lacking systematic integration.

Starting from the inference characteristics of Transformers, it analyzes the sequence dependency in the autoregressive generation phase (new tokens depend on all previous KV representations); it details KV cache management techniques (paged caching, dynamic allocation, compression encoding, etc.) that support long context windows and are the focus of competition among inference engines.

Covers key components of modern inference engines: batching techniques (from static to dynamic batching, improving GPU utilization with code examples); memory optimization techniques (gradient checkpointing, activation recomputation, tensor sharding, etc., enabling large models to run on consumer-grade hardware).

Deployment section: Analyzes the latency/throughput/cost balance of deployment modes such as synchronous services, asynchronous queues, and streaming responses; distributed inference strategies (tensor parallelism, pipeline parallelism, expert parallelism) and multi-node coordination. Optimization section: Quantization techniques (precision-efficiency trade-offs like INT8/INT4); kernel optimization (custom CUDA operators); speculative sampling (draft model prediction + large model verification, improving generation speed and adopted by vLLM and others).

Differentiated learning recommendations: System engineers start with the deployment/optimization sections; algorithm researchers dive into the basics/system sections; full-stack developers read through all sections + code practice. Each chapter is equipped with code examples and exercises, recommending open-source projects like vLLM and Text Generation Inference, and encouraging reading production code.

As an open-source project, it relies on community contributions (submit Issues/PRs to participate in improvements); maintainers are closely connected with the industry, integrating new optimization technologies and hardware features (such as FP8 support for Hopper architecture) to ensure the content is cutting-edge and practical; follow the Release Notes for the latest updates.

LLM Inference Systems fills the gap in systematic resources for this field, providing a solid foundation for building production-grade inference services or academic research; mastering the design principles of inference systems will become a core competency for AI engineers, helping them meet the challenges of the popularization of large model applications.