Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
LLM Inference in C++: Building High-Throughput Engines with PagedAttention and CUDA Kernels

LLM Inference in C++: Building High-Throughput Engines with PagedAttention and CUDA Kernels

S. Lightner, Billie

Paperback

Series: High-Performance C++ Engineering

General ComputersProgramming

ISBN13: 9798259069299
Publisher: Independently Published
Published: Apr 27 2026
Pages: 284
Weight: 1.00
Height: 0.60 Width: 6.69 Depth: 9.61
Language: English
Stop Wasting GPU Compute. Build the High-Throughput, Low-Latency AI Infrastructure of 2026.

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.

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