A 10-Week Roadmap for LLM Inference Serving and Optimization
_jaydeepkarale · x · 2026-08-03
The tweet outlines essential skills for AI engineers deploying LLM inference, highlighting the need to understand decode memory bottlenecks, deploy vLLM/SGLang, master Paged Attention, continuous batching, and quantization tradeoffs.
The linked open-source project time-to-first-token offers a 10-week, 30-minutes-a-day practical roadmap. Consisting of 50 sessions, it guides developers to build an OpenAI-compatible inference service from scratch, covering observability setup, load testing past 1000 concurrent requests, optimization via quantization and speculative decoding, and publishing reproducible benchmarks.
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