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Estimating GPU Memory Needed for LLMs
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Yakakwira Bandwidth Memory (HBM) yakarongedzerwa ndangariro yakaiswa padyo neGPU iyo inopa data nekukurumidza zvakanyanya kupfuura yakajairika RAM.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Ndizvo zvinochengeta AI accelerators kudya, kudzivirira ane simba compute cores kubva pakugara zvisina basa ivo vakamirira maremu emhando uye data.
HBM inogadzirisa bhodhoro rekutanga: yemazuva ano AI machipi anogona kuita matiririyoni ekushanda pasekondi, asi chete kana data rasvika nekukurumidza zvakakwana. Yakajairwa GDDR ndangariro inobatanidza pamusoro pebhazi rakatetepa, nepo HBM inorongedza akawanda DRAM inofa yakatwasuka uye inoabatanidza nezviuru zvemawaya madiki akatwasuka anonzi kuburikidza-silicon vias (TSVs). Aya masheki anogara pasilicon interposer millimeters kubva kuGPU, ichipa yakanyanyisa nzira yedata, funga zviuru zvebhiti kamwechete panzvimbo yemazana. Mhedzisiro ndeye bandwidth kuyerwa mumaterabytes pasekondi. Zvizvarwa zvafambira mberi kubva paHBM2 kuenda kuHBM2e, HBM3, neHBM3e, chimwe nechimwe chichisimudza zvese zviri zviviri kugona nekumhanya. Kune mahombe emitauro mamodheru, ane huremu hunofanirwa kutenderedzwa nguva dzose, HBM huwandu uye bandwidth inowanzova yakakosha kupfuura komputa komputa.
Kubata makumi kana mazana emagigabytes ehuremu hwemhando huru yemutauro padyo neGPU kuitira kuti vagone kufambiswa panguva yega yega nhanho.
Kugonesa NVIDIA H100 uye H200 datacenter GPUs kuti isvike akawanda terabytes pasekondi yendangariro bandwidth yekudzidziswa.
Kusimbisa AI masangano ekudzidzisa uko maGPU mazhinji ega ega anovimba neHBM kudzivirira kumira pakati pematrix mashandiro.
Inotsigira yakakwirira-resolution yekugadzira mufananidzo uye vhidhiyo modhi iyo inofanirwa kufambisa yakakura activation tensor mukati nekubuda mundangariro nekukurumidza.
Memory bandwidth ikozvino inotungamira inomanikidza paAI, saka HBM iri kufambira mberi nekukurumidza. HBM3e iri kutakura mumafambisirwo emagetsi, ine HBM4 iri kumusoro ichivimbisa nzvimbo dzakafara, mastacks akareba, uye huwandu hwakawanda pasuru. Tarisira padyo dhizaini pakati pekurangarira uye pfungwa, pamwe tsika base inofa uye kugadzirisa-pedyo-yeuko, pamwe nemakwikwi anotyisa pakati pevatengesi vakaita seSK hynix, Samsung, uye Micron. Sezvo mamodheru achikura, kuwana data rakawanda padhuze nekombuta, nekukurumidza uye kune yakaderera simba, inoramba iri pakati peAI hardware kufambira mberi.
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Yakakwira Bandwidth Memory (HBM) yakarongedzerwa ndangariro yakaiswa padyo neGPU iyo inopa data nekukurumidza zvakanyanya kupfuura yakajairika RAM. Ndizvo zvinochengeta AI accelerators kudya, kudzivirira ane simba compute cores kubva pakugara zvisina basa ivo vakamirira maremu emhando uye data.
AI machipi anogona kuita matiririyoni ekushanda pasekondi chete kana data yasvika nekukurumidza zvakakwana; HBM inopa iyo bandwidth kuitira kuti macores asafe nenzara.
HBM inorongedza DRAM inofira pamusoro pemumwe uye inoabatanidza nezviuru zvewaya dzakamira (TSVs), ichigadzira nzira yakafara kwazvo yedata.
Panzvimbo pekuvhara nekukasira, HBM inofumura yakafararira interface (1024 bits pastack uye kumusoro) mabyte mazhinji anofambisa kamwechete.
Waya mapfupi pane yakagovaniswa interposer inodzikisa simba rinodiwa pane bhiti rinotamiswa uye kuderedza latency pakati pendangariro uye compute.
LLM inference inoramba ichiyerera hombe huremu matrices, saka ndangariro huwandu uye bandwidth kazhinji inova iyo inomisa chinhu pane compute throughput.
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InoteveraGaidhi rinotevera
Estimating GPU Memory Needed for LLMs
Tekinoroji