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Estimating GPU Memory Needed for LLMs
Xarala
GUIDE teknik
Memoire Bandwidth bu kawe (HBM) mooy mémoire buñ def ci wetu GPU biy joxe done yi gëna gaaw ci RAM yi.
Déglul tegtal biiIA-nettali · ~2 min
It is what keeps AI accelerators fed, preventing the powerful compute cores from sitting idle while they wait for model weights and data.
HBM saafara na jafe-jafe bu yomb: puce IA yu bees yi mën nañu def ay trillion ci operation ci segond bu nekk, waaye sudee done yi yegsi nañu ci lu gaaw. Memoire GDDR buñ miin dafay boole ci bus bu sew, waaye HBM dafay boole DRAM yu bari ci anam wu taxaw ba noppi boole leen ci ay junni fiil yu ndaw yu taxaw yu ñuy woowe through-silicon vias (TSVs). Stack yooyu ñu ngi toog ci kaw interposer silicon millimetre ci GPU bi, di joxe yoonu done bu yaatu lool, xalaat ay junni bit ci benn yoon ci barabu téemeeri. Lépp soo ko boolee mu am yaatuwaayu band bi ñuy natt ci terabyte ci segond bu nekk. Ay jamono joge ci HBM2 dem ba ci HBM2e, HBM3, ak HBM3e, bu nekk di yokk kàttan ak gaawaay. Ci modelu làkk yu mag yi, te dañu wara streaming seeni poid saa yu nekk, kàttanu HBM ak yaatuwaayu bandwidth dañu gëna am solo ci ordinatër bu ñor.
Teg fukki wala téemeeri gigabytes ci diisaay ngir modelu làkk bu mag bu jege GPU bi suko defee ñu mëna streaming ci bépp jéego bu jëm ci inference.
Fexe ba NVIDIA H100 ak H200 GPU yi mëna yegg ci terabyte yu bari ci segond bu nekk ci yaatuwaayu mémoire ngir tàggat.
Xootal clusters tàggat IA fu GPU yu bari ku nekk di wéeru ci HBM ngir moytu taxaw ci diggante liggéey matrix yi.
Jàppale nataal yu am resolusioŋ bu kawe ak modeli wideo yu wara toxal tensor yu mag yuy tàmbali ak genne ci mémoire bi ci lu gaaw.
Yaatu bandwidth mémoire mooy gëna gàllankoor IA, moo tax HBM mingi awaase bu baax. HBM3e mingi yóbbu ci gaawaayu flagship, ak HBM4 ci horizon bi dige interface yu gëna yaatu, stack yu gëna njool, ak kàttan gu gëna bari ci koli bu nekk. Xaarandil jëmmal bu gëna jege diggante mémoire ak logique, amaana base dies ak defar-memoire bu jege, boole ci joŋante bu metti diggante furnisër yu melni SK hynix, Samsung, ak Micron. Lu model yi di màgg, gëna am done yu gëna jege ordinatër, gëna gaaw ak energie bu néew, mooy nekk lu am solo ci yokkuteg hardware IA.
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Memoire Bandwidth bu kawe (HBM) mooy mémoire buñ def ci wetu GPU biy joxe done yi gëna gaaw ci RAM yi. Mooy luy dundal gaawaayu IA yi, di tere core ordinatër yu am doole yi toog rek di xaar diisaayu model yi ak done yi.
Puce IA yi mën nañu def ay trillioŋu liggéey ci segond bu nekk sudee done yi gaaw nañu yegsi; HBM dafay joxe bandwidth boobu suko defee core yi duñu xiif.
HBM dafay boole DRAM yi benn ci kaw beneen bi ba noppi boole leen ak ay junni kable yu taxaw (TSVs), sos yoonu done bu yaatu lool.
Du gëna gaaw, HBM dafay wane interface bu yaatu lool (1024 bits ci stack bu nekk ak ci kaw) ba byte yu bari di toxu benn yoon.
Fiil yu gàtt yi ci kaw interposer buñ bokk dañuy wàññi energie bi ñuy soxla ci bit bu nekk buñ toxal ba noppi wàññi latency bi am ci diggante mémoire bi ak ordinatër bi.
LLM inference dafay wéy di génne matris yu yaatu, moo tax kàttan mémoire ak yaatuwaayu band dañuy faral di nekk facteur limitant bi moo gën ñuy xayma produit yi.
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Ci bi mu melGis bi ci topp
Estimating GPU Memory Needed for LLMs
Xarala