---
title: "LLM inference engineering roadmap"
description: "Learn LLM inference from token generation and KV-cache math to continuous batching, serving runtimes, benchmarking, and capacity planning."
canonical_url: "https://fanout.sh/inference-eng"
md_url: "https://fanout.sh/inference-eng.md"
last_updated: "2026-08-06"
access: "public"
---

# LLM inference engineering roadmap

Learn LLM inference from token generation and KV-cache math to continuous batching, serving runtimes, benchmarking, and capacity planning.

## Public overview

Fanout's LLM inference engineering roadmap is for backend, ML, and platform engineers moving from model demos to production traffic.

The roadmap starts with token generation and workload definition, then covers weights, KV-cache math, continuous batching, PagedAttention, quantization, serving runtimes, distributed execution, benchmarking, and observability.

Every stage ends with a calculation, benchmark, or design artifact so the material stays tied to measurable production decisions.

The full course is still being assembled. The public roadmap, KV-cache calculator, source-led paper explanations, and first practical packet are available as they are published.

---
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