Deep Dive into Kimi K3, Volume III: Systems and Deployment (Foundation Books)
English | October 11, 2026 | ASIN: B0HMNM5MZZ | 858 Pages | Epub | 26.30 MB
How does a model as complex as Kimi K3 actually run-from one GPU kernel to a distributed training system and a serving fleet?Deep Dive into Kimi K3 - Volume III: Systems and Deployment is a detailed technical study of the systems that turn model architecture into computation. It examines GPU kernels, parallel execution, memory management, training infrastructure, and inference serving, with Kimi K3 as the organizing case study.Rather than presenting performance ideas as isolated tricks, the book follows the work itself: which threads own an output, where data is stored, what must be synchronized, how tensor and expert shards communicate, when cached state can be reused, and how correctness is preserved across execution boundaries. Step-by-step numerical examples, resource ledgers, algorithms, and code-level comparisons connect the underlying principles to implementation.Inside this volume, you will explore:GPU kernel foundations: CUDA execution, warp cooperation, tiling, PTX, CUTLASS, CuTE, and asynchronous pipelines.Kimi-specific kernels: FlashKDA, hybrid attention, Gated MLA, Attention Residuals, routed MoE computation, and MoonEP expert parallelism.Distributed training: Data, tensor, pipeline, expert, and context parallelism; training schedules, communication, and memory-efficient execution.Training infrastructure: Activation recomputation and offloading, multimodal input pipelines, checkpoint management, and online model-weight publication.Large-scale agentic RL: Million-token training workflows, resumable rollouts, external prefix state, sandbox execution, and resource coordination.Serving and decoding: Mooncake disaggregated serving, KDA-aware prefix caching, fleet scheduling, failure recovery, and low-precision speculative decoding.What makes this book different?The emphasis is on understanding how systems behave, not memorizing framework names. Worked traces follow values through computation, data placement, synchronization, and state transitions. Correctness checks distinguish sound optimizations from plausible but incorrect shortcuts. Exercises and worked answers reinforce the material.Who should read this book?Researchers, machine learning systems engineers, CUDA developers, graduate students, and advanced readers who want to understand the implementation of large language models. Familiarity with neural-network training and introductory CUDA C++ is recommended; specialized techniques are developed in the book.About the seriesThis is Volume III of a three-volume series. Volume I covers Architecture and Pretraining; Volume II covers Post-Training and Agent Intelligence.This is an independent technical study and is not affiliated with or endorsed by Moonshot AI.
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