Appendix F. Source and Reproducibility Ledger

This edition distinguishes principles, implementation observations, and measurements. A repository path proves that code exists at a snapshot; it does not prove production readiness or performance on every platform.

Edition snapshot

The supplied book informed the coverage audit and standards for approachability. Its prose, diagrams, examples, analogies, and chapter sequence were not reused.

Primary systems papers

• Ashish Vaswani et al., Attention Is All You Need, 2017.
• Woosuk Kwon et al., Efficient Memory Management for Large Language Model Serving with PagedAttention, 2023.
• Lianmin Zheng et al., SGLang: Efficient Execution of Structured Language Model Programs, 2023.
• Gyeong-In Yu et al., Orca: A Distributed Serving System for Transformer-Based Generative Models, OSDI 2022.
• Amey Agrawal et al., Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve, 2024.
• Yinmin Zhong et al., DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving, 2024.
• Pratyush Patel et al., Splitwise: Efficient Generative LLM Inference Using Phase Splitting, 2023.
• Ruoyu Qin et al., Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, 2024.
• Lijie Liu et al., Preble: Efficient Distributed Prompt Scheduling for LLM Serving, 2024.

Kernels, execution, and decoding

• Tri Dao et al., FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness, 2022.
• NVIDIA, CUDA Programming Guide: CUDA Graphs.
• PyTorch, torch.compile reference.
• Yaniv Leviathan, Matan Kalman, and Yossi Matias, Fast Inference from Transformers via Speculative Decoding, 2022.
• Guangxuan Xiao et al., SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models, 2022.

Adapter serving

• Ying Sheng et al., S-LoRA: Serving Thousands of Concurrent LoRA Adapters, 2023.
• Chen Liang et al., Punica: Multi-Tenant LoRA Serving, 2023.
• Edward J. Hu et al., LoRA: Low-Rank Adaptation of Large Language Models, 2021.

Parallel and MoE systems

• Mohammad Shoeybi et al., Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism, 2019.
• Deepak Narayanan et al., Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM, 2021.
• DeepSeek, DeepEP, official expert-parallel communication implementation.

Media and post-training

• Xinyin Ma, Gongfan Fang, and Xinchao Wang, DeepCache: Accelerating Diffusion Models for Free, 2023.
• Feng Liu et al., Timestep Embedding Tells: It’s Time to Cache for Video Diffusion Model, 2024.
• Wei Fu et al., AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning, 2025.
• Shuo-yiin Chang et al., Joint Endpointing and Decoding with End-to-End Models, 2019.
• Loïc Barrault et al., Seamless: Multilingual Expressive and Streaming Speech Translation, 2023.

Standards and operating references

• NVIDIA, DCGM: Topology and NVLink, for distinguishing topology inventory, link state, and measured traffic.
• MLCommons, MLPerf Inference documentation, for scenario, accuracy, and run-rule discipline.
• OpenTelemetry, Semantic conventions, for consistent trace, metric, log, and resource naming.
• NIST, AI Risk Management Framework and Generative AI Profile.
• OWASP, Top 10 for LLM Applications.

Official implementation documentation

• vLLM documentation, including architecture, cache, parallelism, disaggregation, compilation, multimodal, and training integration guides.
• vLLM architecture overview.
• vLLM batch invariance and pooling models.
• SGLang documentation, including attention backends, distributed serving, observability, post-training, and diffusion.
• SGLang deterministic inference, reasoning parsers, embeddings, and prefill/decode disaggregation.
• SGLang Diffusion.

Documentation can describe a release different from the pinned source snapshot. When the two conflict, the manuscript either describes the pinned code or marks the behavior as release-dependent.

Publication tooling

Block diagrams use a purpose-built SVG renderer, with Dagre 3.1.1 providing directed-graph coordinates. Dagre and its license are vendored into the book (assets/vendor/), so diagrams render offline and are not affected by CDN changes. The book controls block dimensions, label wrapping, connectors, decision shapes, and responsive reflow; the diagram definitions remain readable as text if the client-side renderer cannot load. Body, interface, and code typefaces (Literata, Inter, JetBrains Mono) are also vendored as subsets. Diagram definitions remain readable as text if the client-side renderer cannot load.

Implementation map by chapter

Reproducibility status

This manuscript explains how to design experiments but does not claim new performance results. Numeric results cited from papers remain the authors’ results under their published setups. Future editions should attach original benchmark cards, traces, commands, and raw data here, with each claim marked:

• proposed — experiment designed but not run;
• reproduced — run with public artifacts;
• reviewed — independently checked;
• superseded — retained for history but replaced by newer evidence.