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PagedAttention

Allocating each sequence's KV cache as one contiguous block forces you to reserve space for the maximum possible length, which fragments GPU memory and wastes most of it. PagedAttention borrows OS-style paging: it stores the KV cache in fixed-size non-contiguous blocks tracked by a per-sequence block table, so memory is packed, grown on demand, and shareable across sequences. It is the core memory trick that lets vLLM pack far more concurrent sequences into the same VRAM.

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RELATED CONCEPTS

KV Cache Continuous Batching Inference Serving (vLLM, TGI)GPU Memory and VRAM

LESSONS THAT TEACH THIS

Hard DeploymentsBudget weights, resident cache and runtime memory in consistent units30 min→

PRACTICE THIS IN REAL QUESTIONS

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