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Interactive 3D/Continuous Batching & PagedAttention
GPU Util (Static)
44%
GPU Util (Cont.)
55%
Throughput ↑
1 → 1 req/s
P50 Latency ↓
355 → 234 ms
Controls
Requests10
420
Max Batch Size4
28
Seq Length256
1284096
Options
Legend
Active processing
Wait (static: batch not full)
GPU slot (idle)
Continuous batching inserts new requests the moment a slot frees - GPU never waits for a full batch. vLLM uses this + PagedAttention to achieve near-100% GPU utilization.

Continuous Batching & PagedAttention - Interactive Visualization

Static batching waits for a full batch before starting, wasting GPU time when sequences finish early. Continuous batching inserts new requests the moment a slot frees, dramatically improving throughput. PagedAttention eliminates KV cache fragmentation by storing cache in non-contiguous memory pages, like OS virtual memory. Together they power vLLM.

  • Static batching timeline: GPU sits idle when early-finishing sequences create empty slots
  • Continuous batching: new requests inserted immediately as slots open, GPU stays busy
  • KV cache fragmentation: standard allocation wastes memory on reserved but unused space
  • PagedAttention: non-contiguous memory pages eliminate fragmentation and increase batch size

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.