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Inference Engineering

The definitive interactive guide to AI model inference — GPU hardware, inference engines (vLLM, SGLang, TensorRT-LLM), quantization, KV caching, and production autoscaling. Written by Philip Kiely, published by Baseten Books.

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# Inference Engineering

> The definitive interactive guide to AI model inference — GPU hardware, inference engines (vLLM, SGLang, TensorRT-LLM), quantization, KV caching, and production autoscaling. Written by Philip Kiely, published by Baseten Books.

## About

Inference Engineering is an interactive technical guide covering everything needed to deploy and optimize AI models in production. The site combines reading chapters, interactive calculators, quizzes, and guided learning paths.

- [Inference Engineering Home](https://inferenceengineering.tech/): Full interactive guide to AI inference engineering
- [Learning Paths](https://inferenceengineering.tech/paths/): Structured learning paths for different engineer types
- [Benchmarks](https://inferenceengineering.tech/benchmarks/): Real H100 benchmark data comparing inference engines
- [Interactive Exercises](https://inferenceengineering.tech/exercises/): Calculators and simulators for inference engineering

## Chapters

- [Preface: Why Inference Engineering Matters](https://inferenceengineering.tech/chapters/preface/): The explosive growth of open models and why inference engineering is the most important skill in AI.
- [Chapter 0: Inference — The Three Layers of Inference](https://inferenceengineering.tech/chapters/inference/): Introduces the three layers of inference: runtime, infrastructure, and tooling. A map of the entire book.
- [Chapter 1: Prerequisites — Before You Optimize](https://inferenceengineering.tech/chapters/prerequisites/): Use case definition, latency and cost budgeting, model selection and evaluation, and fine-tuning for quality.
- [Chapter 2: Models — Architecture and Bottlenecks](https://inferenceengineering.tech/chapters/models/): Technical architecture of LLMs and diffusion models — transformers, attention, MoE, and inference bottlenecks.
- [Chapter 3: Hardware — GPUs and Accelerators](https://inferenceengineering.tech/chapters/hardware/): GPU architecture, compute and memory, NVIDIA generations (Hopper, Blackwell, Rubin), instances, and alternatives.
- [Chapter 4: Software — From CUDA to Inference Engines](https://inferenceengineering.tech/chapters/software/): CUDA kernels, PyTorch, model formats, inference engines (vLLM, SGLang, TensorRT-LLM), NVIDIA Dynamo, and benchmarking.
- [Chapter 5: Techniques — Optimization Deep Dives](https://inferenceengineering.tech/chapters/techniques/): Quantization, speculative decoding, KV cache re-use, model parallelism, and disaggregation in practice.
- [Chapter 6: Modalities — Beyond Text](https://inferenceengineering.tech/chapters/modalities/): Vision language models, embeddings, ASR, TTS, image generation, and video generation inference.
- [Chapter 7: Production — Ship It](https://inferenceengineering.tech/chapters/production/): Containerization, autoscaling, multi-cloud, deployment, observability, and client code for production inference.

## Learn Articles

- [vLLM vs SGLang vs TensorRT-LLM](https://inferenceengineering.tech/learn/v
…

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