LLM Inference in C++: Building High-Throughput Engines with PagedAttention and CUDA Kernels (High-Performance C++ Engineering)
Language: English
Published by Independently published, 2026
- Softcover
- New
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- Title
- LLM Inference in C++: Building High-Throughput Engines with PagedAttention and CUDA Kernels (High-Performance C++ Engineering)
- Author
- S. Lightner, Billie
- Publisher
- Independently published
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798259069299
- Series
- Book 7 of 11: High-Performance C++ Engineering
The "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.
Focusing 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.
Inside, 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 & 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.
Built 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.
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California Books
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