This guide walks through building a speculative decoding engine in pure Python, including draft-model verification and KV-cache sharing. It’s ideal for showcasing systems skills to hiring managers.
A hands-on guide to building a speculative decoding engine with a draft model and verifier in pure Python. This project signals deep systems expertise and is practical enough to actually complete, with real runnable code at every step.
This project demonstrates end‑to‑end inference engineering, from language modeling to low‑latency token generation, and signals strong systems skills to hiring managers.
Build a pure‑Python speculative decoding engine that uses a draft LLM and parallel acceptance‑reject sampling to speed up text generation. This project highlights real systems engineering skills for your CV.
Learn to implement a compact LLM inference engine that showcases advanced systems skills, from KV cache reuse to tree-attention speculative decoding.