As an AI engineer, please learn:
A comprehensive 10-week roadmap for engineers to learn how to run LLM inference in production, not just read about it. The program consists of 50 sessions, each contributing to the creation of an OpenAI-compatible inference service deployed on a rented GPU. This service undergoes instrumentation, load testing beyond 1000 concurrent requests, optimization with quantization and speculative decoding, placement of a cost-aware router, and publication as a reproducible benchmark.
Participants should have a solid understanding of Python, transformers at the architecture level, and the command line. Prior serving, Kubernetes, or CUDA experience is not required. The program begins with the roofline model, followed by a series of optimization sessions, including quantization, continuous batching, speculative decoding, and disaggregation.
These optimizations are interconnected, with each subsequent step building upon the previous one. Measurement and optimization are conducted in parallel, with instrumentation introduced in week 3 and load testing in week 5. The final deliverable is an OpenAI-compatible endpoint serving a 7-8B model, accompanied by notes explaining PagedAttention and the V1 scheduler, as well as a live Grafana dashboard displaying key performance metrics.
Written by urgent.news from Daily Dose of DS's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
