vLLM PagedAttention & Continuous Batching

vLLM High-Throughput Inference Engine (PagedAttention Virtual Memory & Continuous Iteration-Level Batching) provides the standard open-source serving architecture achieving 96%+ GPU memory utilization and 3-5x serving throughput gains over naive runtimes; its two foundational breakthroughs: 1) PagedAttention: inspired by OS virtual memory paging, partitioning dynamically expanding KV Caches into fixed-size physical blocks (e.g. 16 tokens/block) managed via a logical-to-physical Page Table, eliminating both internal and external memory fragmentation and slashing memory waste from 60%-80% down to $<4\%$; 2) Continuous (Iteration-Level) Batching: scheduling requests at the single autoregressive step granularity, dynamically admitting new arrivals and evicting finished sequences on every forward pass, eliminating batch pipeline idle bubbles entirely.

Production enterprise private LLM clusters, high-concurrency API gateways, and ultra-responsive streaming generation services.

Naive inference engines suffer massive memory fragmentation where a single A100 GPU crashes at 4-8 concurrent streams; vLLM powers 64-128 concurrent streams on the same hardware, fully saturating memory bandwidth.

Diagram the logical-to-physical KV cache block allocation and address translation managed by vLLM's Page Table?

Explain how iteration-level scheduling in Continuous Batching eliminates token generation bubbles inherent in static batching?

Explain how PagedAttention's Copy-on-Write (CoW) enables zero-copy prompt KV cache sharing across parallel branches in Beam Search?

How does Chunked Prefill interleave long prompt chunks with decode tokens to eliminate TTFT latency spikes?

Analyze NCCL communication overheads of Tensor Parallelism vs Pipeline Parallelism when serving 70B models on multi-GPU nodes in vLLM?