[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODAzMjM","title","Build an LLM Inference Engine in C++","state","published","contactAuthorMethod","email","aboutText","

Build an LLM Inference Engine in C++ — Through Challenges

You don't truly understand how large language models run

until you've built the engine yourself.

This book takes you from a blank C++ project to a complete,

working inference engine that loads a real Llama-family model

and generates text — one challenge at a time.

What you'll build:

- A strided tensor system with zero-copy views and arena allocation

- Math kernels: RMSNorm, SwiGLU, softmax, GEMM with SIMD

- A byte-level BPE tokenizer

- A full Transformer: RoPE, GQA, Flash Attention, KV Cache

- int8/int4 quantization with direct block multiplication

- GGUF model loading with mmap

- Sampling, streaming, speculative decoding, and continuous batching

- An optional CUDA capstone for the heaviest kernels

By Unit 14, the engine runs a real model on CPU. Every concept

earns its place right after you've built the thing it improves.

Who this is for: C++20 developers comfortable with algorithms

and memory layout who want to understand what actually happens

inside an LLM runtime — not by reading, but by building.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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