{"product_id":"llm-inference-in-c-building-high-throughput-engines-with-pagedattention-and-cuda-kernels-9798259069299","title":"LLM Inference in C++: Building High-Throughput Engines with PagedAttention and CUDA Kernels","description":"\u003cp\u003e • Author(s): Billie S. Lightner\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming Languages - C++\u003c\/p\u003e\u003cp\u003e\u003cb\u003eStop Wasting GPU Compute. Build the High-Throughput, Low-Latency AI Infrastructure of 2026.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eThe \"VRAM Wall\" is the biggest bottleneck in modern AI. Standard Python wrappers and out-of-the-box runtimes are fine for prototyping, but at scale, memory fragmentation and Global Interpreter Lock (GIL) overhead will destroy your throughput. \u003cb\u003eLLM Inference in C++\u003c\/b\u003e is the definitive engineering manual for bypassing Python entirely and building custom, bare-metal inference engines that maximize hardware utilization. \u003cp\u003e\u003c\/p\u003eFocusing on the cutting-edge 2026 landscape, this book bridges the gap between high-level AI concepts and low-level GPU execution. You will learn how to implement enterprise-grade features like \u003cb\u003ePagedAttention, FlashAttention-3, and Continuous Batching\u003c\/b\u003e directly in C++ and CUDA, unlocking massive performance gains for large-scale language models.\u003cbr\u003eInside, you will discover: \u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eHardware-Aware Memory Management: \u003c\/b\u003e Eliminate memory waste by implementing PagedAttention logic and custom allocators to bypass std:: malloc overhead.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eAccelerated Tensor Algebra: \u003c\/b\u003e Master C++23's std:: mdspan and write fused SIMD kernels with AVX-512 to minimize GPU context switching.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eCustom CUDA Kernels: \u003c\/b\u003e Write high-speed FlashAttention-3, LayerNorm, and RMSNorm kernels while managing CUDA streams for maximum GPU occupancy.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eThe Cost Killer (Quantization): \u003c\/b\u003e Slash VRAM requirements with bit-level manipulation for 4-bit (AWQ) and 8-bit (FP8) inference using NVIDIA Tensor Cores.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eDistributed \u0026amp; Speculative Execution: \u003c\/b\u003e Scale across clusters using zero-copy NCCL\/RDMA interconnects and implement Draft Models to accelerate massive architectures.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eThe Production Serving Layer: \u003c\/b\u003e Build lock-free C++ request queues for continuous batching and track P99 \"Time to First Token\" (TTFT) at the systems level.\u003c\/li\u003e\n\u003c\/ul\u003eTHE IMPLEMENTATION VAULT (Appendix) \u003cp\u003e\u003c\/p\u003eBuilt for the infrastructure engineer in the trenches, the Appendix provides immediate, battle-tested utility: \u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eThe 15-Point Production-Ready Checklist: \u003c\/b\u003e Your mandatory safety and performance audit before deploying any custom engine.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eLatency vs. Throughput Reference Table: \u003c\/b\u003e The ultimate cheat sheet for balancing batch sizes against user wait times.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eTroubleshooting Guide: \u003c\/b\u003e Direct solutions for the top 10 most common and devastating CUDA and C++ memory errors.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eDon't let inefficient software architecture throttle your hardware. Master C++ LLM inference and build the fastest, most cost-effective AI engines in the industry.\u003c\/b\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47883298209943,"sku":"9798259069299","price":2569.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798259069299.webp?v=1781100936","url":"https:\/\/atlanticbooks.com\/products\/llm-inference-in-c-building-high-throughput-engines-with-pagedattention-and-cuda-kernels-9798259069299","provider":"Atlantic Books","version":"1.0","type":"link"}