Unverified60% confidenceFactTime unknown

vLLM的PagedAttention技术将显存碎片率从传统方案的60%-80%降至不足4%

2

Sources

60%

Confidence

Long-term

Relevance

6/1/2026

First Seen

Sources

vLLM与SGLang本地部署教程：性能提升3-8倍的实战指南

bilibili尚书省说书人

Related Entities

vLLMproduct PagedAttentionconcept

Related Claims

Unverified使用Unsloth进行LoRA/QLoRA微调时，显存占用可减少约50%-70%67% similar Verified量化技术可将模型体积压缩50%-75%，在损失极小精度前提下降低显存占用64% similar Unverified提供设计图比纯文字描述能减少约60%-70%的后续修改轮次64% similar UnverifiedRMSNorm相比原始LayerNorm省去均值中心化步骤，将归一化计算量降低约40%64% similar Unverified通过INT8或INT4量化，可将模型显存占用降低50%至75%63% similar

Cite This Claim

Stable URI

https://kongchang.com/claim/18023

API

curl https://kongchang.com/api/v1/knowledge/claims/18023

MCP

get_claim(id=18023)