This repository implements Speculative Speculative Decoding (SSD), a novel LLM inference algorithm that enables parallel execution of draft and target models on separate hardware. The system provides a complete inference engine with tensor parallelism support, optimized kernels (FlashInfer, CUDA graphs), and advanced KV caching techniques. It includes benchmarking tools to compare performance against baselines like SGLang and vLLM, and supports both synchronous and asynchronous speculative decoding modes. The codebase is well-structured with clear separation of concerns between model implementations, engine components, and utility functions.
This repository is a curated collection of learning materials for AI infrastructure, specifically focused on large language model (LLM) inference optimization. It contains detailed markdown documents covering core topics such as vLLM quickstart, PagedAttention, Prefix Caching, Speculative Decoding, Chunked-Prefills, and Prefill-Decode (PD) Disaggregation. Each lesson includes in-depth explanations, diagrams, code examples, and references to relevant papers and talks, making it a comprehensive resource for engineers and researchers seeking to understand and apply state-of-the-art LLM serving techniques. The content is organized as a series of study sessions, with each lesson building on previous ones. The repository does not contain any runnable application code or data pipelines; it is purely educational material. However, the code snippets provided (e.g., Python scripts for vLLM offline and online inference) are directly usable and demonstrate practical usage of the discussed technologies. The documentation is well-structured, with clear learning objectives, prerequisites, and supplementary resources. While the repository itself is not a software application, its value lies in the depth and clarity of its technical explanations. It serves as an excellent reference for anyone deploying or optimizing LLM inference systems, and the content is actively maintained with recent commits and issue tracking. The absence of reusable software components is expected given its educational nature, but the quality of the documentation is high, earning a strong quality score.