Fast LLM Serving with vLLM and PagedAttention
Anyscale. Walks through why naive LLM serving wastes 60–80% of GPU memory, how PagedAttention borrows OS-style paging to fix that, and why continuous batching produces the 24× throughput numbers the article uses in its math. After this, the article's "you'll be lucky to hit 50% utilisation" line stops feeling abstract.
AI Expert note
The PagedAttention mental model remains important, but throughput numbers and serving-engine defaults age quickly. Use this for fundamentals, then benchmark your own model, hardware, batch shape and uptime requirements before making a hosting decision.
What you should get from this
Understand why serving engines, batching and KV-cache memory dominate self-hosted inference economics.
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Basic knowledge of transformer inference, GPU memory limits and API-vs-self-hosting tradeoffs.
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