vllm-uses-pagedattention-and-continuous-batching

Summary

vLLM runs large language models for many users at once by borrowing two tricks from operating systems: it manages GPU memory in small pages so nothing gets wasted, and it keeps slotting new requests in as old ones finish rather than waiting for a full batch to complete. This matters because it sets the practical benchmark for how fast and memory-efficient LLM serving can be, and any system claiming to compete needs to match those two specific optimizations.