README.md

metadata

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
• How much GPU memory do you need to serve a 70B model?
• Inference engineer roadmap: skills, tools, and a 12-week plan
• KV cache math
• LLM inference interview questions with worked answers
• Prefill vs decode
• Quantization for serving: FP8, INT8, INT4 tradeoffs
• Speculative decoding: when it helps and when it doesn't
• Time-to-first-token vs tokens-per-second: what to measure
• vLLM vs SGLang vs TensorRT-LLM: when to pick which
• KV cache calculator (README) — 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.