---
license: cc-by-4.0
language:
- en
pretty_name: LLM Inference Engineering Notes
tags:
- text
- llm-inference
- vllm
- sglang
- kv-cache
- quantization
task_categories:
- text-generation
size_categories:
- n<1K
---

# LLM Inference Engineering Notes

A small collection of technical notes (Markdown text) on serving large language models: KV-cache math, prefill vs decode, continuous batching, quantization, speculative decoding, GPU memory sizing, latency metrics, engine comparisons and interview prep. Includes a dependency-free KV-cache / GPU-memory calculator script. This is a notes collection, not a training dataset.

## Table of contents

- [Continuous batching explained with numbers](articles/continuous-batching-explained.md)
- [How much GPU memory do you need to serve a 70B model?](articles/gpu-memory-for-70b.md)
- [Inference engineer roadmap: skills, tools, and a 12-week plan](articles/inference-engineer-roadmap.md)
- [KV cache math](articles/kv-cache-math.md)
- [LLM inference interview questions with worked answers](articles/llm-inference-interview-questions.md)
- [Prefill vs decode](articles/prefill-vs-decode.md)
- [Quantization for serving: FP8, INT8, INT4 tradeoffs](articles/quantization-for-serving.md)
- [Speculative decoding: when it helps and when it doesn't](articles/speculative-decoding.md)
- [Time-to-first-token vs tokens-per-second: what to measure](articles/ttft-vs-tokens-per-second.md)
- [vLLM vs SGLang vs TensorRT-LLM: when to pick which](articles/vllm-vs-sglang-vs-tensorrt-llm.md)
- [KV cache calculator](calculator/kv_calc.py) ([README](calculator/README.md)) — interactive version: https://huggingface.co/spaces/inference-engineering-academy/llm-kv-cache-calculator

Made by Inference Engineering Academy (https://www.inference-engineering.xyz/), which has a free Foundations course on LLM inference.