DeepOffer

Explain PagedAttention and the KV-cache fragmentation problem it solves.

ML System DesignReported interview question
Reported in public interview compilations — NVIDIA, Together AI

PagedAttention maps logical KV blocks to noncontiguous physical pages, much like virtual memory. It reduces fragmentation, enables prefix sharing and copy-on-write, and lets the scheduler pack more sequences safely.

Make interfaces and ownership explicit; add versioning, access control, monitoring, canary rollout, rollback, and a plan for delayed labels or human review.

Common follow-up questions

Practice this question with an AI interviewer

Get asked follow-ups live, then receive a scored report — like a real MLE interview loop.

Start AI mock interview