Unverified50% confidenceFactExact time

PagedAttention cuts the KV Cache into fixed-size blocks, allocated on demand and stored non-contiguously, raising VRAM utilization from the traditional 20%–40% to over 90%.

1

Sources

50%

Confidence

Long-term

Relevance

10/12/2026

First Seen

Sources

Local LLM Deployment in Practice: A Deep Comparison of Ollama, llama.cpp, and vLLM

Related Entities

PagedAttentionconcept vLLMproduct

Related Claims

UnverifiedPagedAttention divides the KV Cache into fixed-size blocks, allocates them dynamically on demand, and allows blocks from different requests to be stored non-contiguously.79% similar UnverifiedvLLM的PagedAttention将KV缓存切分为固定大小物理块（通常16个token），使显存利用率从不足40%提升至接近90%78% similar VerifiedPagedAttention splits the KV Cache into fixed-size physical blocks mapped via a block table, and can improve GPU utilization from below 40% to above 90%.78% similar VerifiedPagedAttention improves KV Cache memory utilization from below 40% to close to 100%.78% similar UnverifiedThis update adds Paged KV cache, improving cache utilization and eliminating the need for full prefill every time75% similar

Cite This Claim

Stable URI

https://kongchang.com/claim/1008113

API

curl https://kongchang.com/api/v1/knowledge/claims/1008113

MCP

get_claim(id=1008113)