How to start learning inference engineering: a roadmap beyond the viral green book
kmeanskaran · x · 2026-09-08
A guide responding to the viral Inference Engineering green book by Philip Kiely, which gave scattered problems—GPU memory, batching, latency, cost per token—one name and one map but skipped the entry questions.
This article covers: what the field is and contains, how much ML is genuinely mandatory, whether it's a separate career or a corner of your existing one, what to learn before starting, and in what order. No code, no walkthrough—a pure roadmap.
More from Infra
- Both Nvidia and Google run ARC synthetic data generators — GregKamradt · 2026-09-08
- Memory capex to hit $97B in 2026, $146B in 2027 — 56% of next year's semiconductor spend — Beth_Kindig · 2026-09-08
- TokenSpeed stabilizes Kimi K3 on ~1,000 B300 GPUs for 3+ weeks: GB300 TP8 optimization details — zhyncs42 · 2026-09-08
- KV Cache Math From Scratch: A Deep Workshop on LLM Inference at Scale — AI Engineer · 2026-09-08
- Intel extends High NA EUV lead as TSMC and Samsung confirm adoption by end of decade — BenBajarin · 2026-09-08
- User seeks a custom GGUF quant to fit GLM on a 192 GB RAM Mac between Q2 and Q4 — CentrifugalMalaise · 2026-09-08