Okuqondile komkhumbi weMicron ongu-512GB kubalulekile kunengqalasizinda ye-AI kunama-PC.
Kuthiwa iMicron ifinyelele umgomo wendlela yokugcina engu-512GB DDR5 futhi ilindele ukukhiqizwa ngobuningi kwe-512GB RDIMM engxenyeni yesibili ka-2027. Bengingakuthatha isikhathi njengoba kubikwe kunokuba kube isiqondiso esiqinisekiswe ngokuzimela okwamanje.
Okucacile kakade okuvela eMicron ukuthi inkampani iyahambisa inkumbulo yamaseva iye kumakhono aphezulu kakhulu. Ikhiphe amasampula angu-256GB DDR5 RDIMMs asebenzisa i-1-gamma DRAM nokuhlanganisa okuthuthukile kokupakisha kwe-3D, anezivinini kuze kufike ku-9,200 MT/s. IMicron ithi i-module eyodwa engu-256GB inganciphisa amandla okusebenza ngaphezu kuka-40% uma kuqhathaniswa nama-module amabili angu-128GB.
Engikuthathayo ukuthi umthamo wenkumbulo uya uba yingxenye ye-equation yokukhuphula i-AI, hhayi nje imininingwane yengxenye.
Njengoba amamodeli nomsebenzi wokucabanga (inference) kukhula, amaseva adinga umthamo wenkumbulo omkhulu eceleni kokusebenza kwe-GPU kanye ne-bandwidth. Ama-RDIMM anomthamo ophakeme anganciphisa inani lama-module adingekayo ukuze kufinyelelwe umthamo othize, okungenzeka kuthuthukise amandla nokusebenza kahle kwesistimu yonke.
Kodwa kukhona umehluko: umthamo omkhulu wedwa awuwaxazululi ama-bottleneck engqalasizinda ye-AI. I-bandwidth, i-latency, amandla, ukupakisha, i-CPU architecture kanye nezindleko zesistimu iyonke kusabalulekile.
Yingakho ngibheka ngokucophelela inqubekelaphambili yeMicron ukusuka ekuthatheni amasampula kuya ekukhiqizeni. Ubufakazi bangempela buzoba wukuthi inkumbulo enomthamo ophakeme iyahamba yini isuka ekubeni yisinyathelo sobunjiniyela iye ekuthunyelweni okubalulekile ngokwezentengiselwano.
Lokho kushintsha kungasisiza siqonde okuningi mayelana nokuthi ukutshalwa kwezimali kwengqalasizinda ye-AI kuya kuphi ngempela.
Kuthiwa iMicron ifinyelele umgomo wendlela yokugcina engu-512GB DDR5 futhi ilindele ukukhiqizwa ngobuningi kwe-512GB RDIMM engxenyeni yesibili ka-2027. Bengingakuthatha isikhathi njengoba kubikwe kunokuba kube isiqondiso esiqinisekiswe ngokuzimela okwamanje.
Okucacile kakade okuvela eMicron ukuthi inkampani iyahambisa inkumbulo yamaseva iye kumakhono aphezulu kakhulu. Ikhiphe amasampula angu-256GB DDR5 RDIMMs asebenzisa i-1-gamma DRAM nokuhlanganisa okuthuthukile kokupakisha kwe-3D, anezivinini kuze kufike ku-9,200 MT/s. IMicron ithi i-module eyodwa engu-256GB inganciphisa amandla okusebenza ngaphezu kuka-40% uma kuqhathaniswa nama-module amabili angu-128GB.
Engikuthathayo ukuthi umthamo wenkumbulo uya uba yingxenye ye-equation yokukhuphula i-AI, hhayi nje imininingwane yengxenye.
Njengoba amamodeli nomsebenzi wokucabanga (inference) kukhula, amaseva adinga umthamo wenkumbulo omkhulu eceleni kokusebenza kwe-GPU kanye ne-bandwidth. Ama-RDIMM anomthamo ophakeme anganciphisa inani lama-module adingekayo ukuze kufinyelelwe umthamo othize, okungenzeka kuthuthukise amandla nokusebenza kahle kwesistimu yonke.
Kodwa kukhona umehluko: umthamo omkhulu wedwa awuwaxazululi ama-bottleneck engqalasizinda ye-AI. I-bandwidth, i-latency, amandla, ukupakisha, i-CPU architecture kanye nezindleko zesistimu iyonke kusabalulekile.
Yingakho ngibheka ngokucophelela inqubekelaphambili yeMicron ukusuka ekuthatheni amasampula kuya ekukhiqizeni. Ubufakazi bangempela buzoba wukuthi inkumbulo enomthamo ophakeme iyahamba yini isuka ekubeni yisinyathelo sobunjiniyela iye ekuthunyelweni okubalulekile ngokwezentengiselwano.
Lokho kushintsha kungasisiza siqonde okuningi mayelana nokuthi ukutshalwa kwezimali kwengqalasizinda ye-AI kuya kuphi ngempela.