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llama.cpp versus vLLM: what is each engine built for, and when does the CPU or Apple Silicon path actually win?

One engine is built to run a model anywhere with one user; the other is built to share a datacenter GPU across hundreds. The answer turns that into memory-bandwidth arithmetic and finds the cases where the laptop wins.

Updated Oct 2026 · Worked answers on inference mechanisms, with calculations, implementation details, and follow-up questions.

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Next in this trackWrite the main loop of a minimal LLM serving engine: request queue, scheduler, model step, sampling and streaming.Next in this trackWhat changed in vLLM's Model Runner V2 compared with the V1 runner, how does the engine decide which one to use, and what falls back?Next in this trackYou are serving a reasoning model whose answers run 30K output tokens. Which engine do you pick, and how do you configure it?

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