KV kaṣe adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Kí ni KV cache fipamọ?
   • Bọtini ati awọn olutọpa iye ṣe iṣiro fun awọn ami ami iṣaaju
   • Awọn awoṣe ká kikun àdánù matrices
   • Itan iwiregbe olumulo bi ọrọ itele
   • Ik o wu awọn iṣeeṣe
2. Isoro wo ni kaṣe KV ni akọkọ yanju?
   • O ṣe ilọsiwaju ilo ọrọ ti ipilẹṣẹ
   • O yago fun awọn bọtini atunto ati awọn iye fun gbogbo awọn ami ami iṣaaju ni gbogbo igbesẹ
   • O mu ki awọn fokabulari awoṣe
   • O encrypts ibaraẹnisọrọ
3. Kini akọkọ isalẹ ti kaṣe KV?
   • O mu ki iran losokepupo
   • O din o wu didara
   • Lilo iranti rẹ dagba laini pẹlu ipari ọrọ-ọrọ
   • O ṣiṣẹ nikan lori awọn CPUs
4. Ilana wo ni o dinku iwọn kaṣe KV nipasẹ pinpin awọn bọtini ati awọn iye kọja awọn ori akiyesi?
   • Jade kuro
   • Iwadi ina
   • Àmi
   • Ibeere akojọpọ tabi akiyesi ibeere pupọ
5. Kini awọn ipele meji ti itọkasi transformer ni ibatan si kaṣe KV?
   • Ṣaju (ṣe ilana naa) ati pinnu (ṣe ami ami kan ni akoko kan)
   • Ikẹkọ ati idanwo
   • Encode ati compress
   • Po si ati download
6. Kini Ifarabalẹ Paged vLLM ṣe fun kaṣe KV naa?
   • Npa kaṣe kuro lẹhin aami kọọkan
   • Yato kaṣe ni awọn bulọọki ti kii ṣe contiguous lati dinku pipin iranti
   • Yi kaṣe pada sinu ọrọ
   • Ilọpo meji kika paramita awoṣe
7. Why can KV-cache memory limit how many requests a GPU serves concurrently?
   • The cache stores all training examples on the GPU
   • Each request creates a new model copy
   • Each active sequence retains key and value tensors that grow with its generated context
   • The cache stores only final text on disk
8. What is one benefit of sharing KV-cache blocks among requests with a common prefix?
   • It makes unrelated prefixes identical
   • It removes the need to compute attention
   • It guarantees lower latency in every workload
   • It can avoid storing duplicate prefix state and reduce memory use