---
title: "Labs & capstones"
description: "Curriculum labs, interactive tools, and capstone projects for inference engineering."
canonical_url: "https://fanout.sh/inference-eng/practice"
md_url: "https://fanout.sh/inference-eng/practice.md"
access: "public"
---

# Labs & capstones

Curriculum labs, interactive tools, and capstone projects for inference engineering.

## Public overview

Observable mini inference engine: Build a small serving engine with a paged KV allocator, continuous batching, chunked prefill, preemption, and prefix sharing, then make every scheduler decision replayable from a trace. It proves you understand the state machine that vLLM and SGLang are built on, not just their flags.

Reproducible benchmark laboratory: Build an endpoint qualification harness that validates output semantics, drives open-loop and closed-loop load, records every request and error, and captures a run manifest. It proves you can produce a latency number another engineer can reproduce and trust.

Hardware and model fit planner: Build a decision artifact that takes exact GPU SKUs, weight formats, runtime overhead, KV capacity, and a target SLO and returns a GPU count, a cost per million tokens, and an uncertainty band. It proves you can turn arithmetic into a purchase recommendation and then check it against a measurement.

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