{"product_id":"b0gynpjr32","title":"LLM Inference in C++: Building High-Throughput Engines with PagedAttention and CUDA Kernels (High-Performance C++ Engineering)","description":"\u003cp\u003eStop Wasting GPU Compute. Build the High-Throughput, Low-Latency AI Infrastructure of 2026.\u003c\/p\u003e\n\n\u003cp\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.  LLM Inference in C++  is the definitive engineering manual for bypassing Python entirely and building custom, bare-metal inference engines that maximize hardware utilization.\u003c\/p\u003e\n\n\u003cp\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  PagedAttention, FlashAttention-3, and Continuous Batching  directly in C++ and CUDA, unlocking massive performance gains for large-scale language models.\u003cbr\u003e\nInside, you will discover: Hardware-Aware Memory Management:  Eliminate memory waste by implementing PagedAttention logic and custom allocators to bypass std::malloc overhead. Accelerated Tensor Algebra:  Master C++23's std::mdspan and write fused SIMD kernels with AVX-512 to minimize GPU context switching. Custom CUDA Kernels:  Write high-speed FlashAttention-3, LayerNorm, and RMSNorm kernels while managing CUDA streams for maximum GPU occupancy. The Cost Killer (Quantization):  Slash VRAM requirements with bit-level manipulation for 4-bit (AWQ) and 8-bit (FP8) inference using NVIDIA Tensor Cores. Distributed \u0026amp; Speculative Execution:  Scale across clusters using zero-copy NCCL\/RDMA interconnects and implement Draft Models to accelerate massive architectures. The Production Serving Layer:  Build lock-free C++ request queues for continuous batching and track P99 Time to First Token (TTFT) at the systems level. THE IMPLEMENTATION VAULT (Appendix)\u003c\/p\u003e\n\n\u003cp\u003eBuilt for the infrastructure engineer in the trenches, the Appendix provides immediate, battle-tested utility: The 15-Point Production-Ready Checklist:  Your mandatory safety and performance audit before deploying any custom engine. Latency vs. Throughput Reference Table:  The ultimate cheat sheet for balancing batch sizes against user wait times. Troubleshooting Guide:  Direct solutions for the top 10 most common and devastating CUDA and C++ memory errors. Don'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\/p\u003e","brand":"ANT","offers":[{"title":"Ebook","offer_id":54231226483051,"sku":"YSL26514","price":15.33,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0978\/1674\/0203\/files\/716tcqct4JL._SY1500.jpg?v=1792834037","url":"https:\/\/www.booktavero.com\/products\/b0gynpjr32","provider":"BookTavero","version":"1.0","type":"link"}