{"id":7271,"date":"2023-11-10T06:57:38","date_gmt":"2023-11-10T06:57:38","guid":{"rendered":"https:\/\/dailyai.com\/?p=7271"},"modified":"2023-11-10T07:08:17","modified_gmt":"2023-11-10T07:08:17","slug":"nvidia-sets-new-ai-training-records-in-mlperf-benchmarks","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/","title":{"rendered":"Nvidia setter nye rekorder for AI-trening i MLPerf-benchmarks"},"content":{"rendered":"

Nvidia skalerte og optimaliserte Eos AI-superdatamaskinen for \u00e5 sette nye rekorder for MLPerf AI-trening.<\/strong><\/p>\n

Da Nvidia slo p\u00e5 AI-superdatamaskinen Eos i mai, ble 10 752 NVIDIA H100 Tensor Core GPU-er vekket til live, og de har nettopp blitt testet i den virkelige verden.<\/p>\n

Denne enest\u00e5ende prosessorkraften sammen med nye programvareoptimaliseringer har gjort det mulig for Eos \u00e5 presse MLPerf m\u00e5lestokk<\/a> inn i rekordterritorium.<\/p>\n

MLPerf-referansen med \u00e5pen kildekode er et sett med trenings- og inferenstester som er utviklet for \u00e5 m\u00e5le ytelsen til maskinl\u00e6ringsarbeidsbelastninger p\u00e5 virkelige datasett.<\/p>\n

Et av de fremste resultatene var at Eos klarte \u00e5 trene opp en GPT-3-modell med 175 milliarder parametere p\u00e5 \u00e9n milliard tokens p\u00e5 bare 3,9 minutter.<\/p>\n

Da Nvidia satte rekorden p\u00e5 denne m\u00e5lestokken for mindre enn 6 m\u00e5neder siden, tok det nesten tre ganger s\u00e5 lang tid med en tid p\u00e5 10,9 minutter.<\/p>\n

Nvidia klarte ogs\u00e5 \u00e5 oppn\u00e5 en effektivitet p\u00e5 93% under testene, noe som betyr at de brukte nesten all datakraften som teoretisk sett var tilgjengelig i Eos.<\/p>\n

Microsoft Azure, som bruker omtrent det samme H100-oppsettet som Eos i sin virtuelle ND H100 v5-maskin, kom innenfor 2% av Nvidias testresultater i sine MLPerf-tester.<\/p>\n

\"\"
Nvidia-maskinvare brukt i MLPerf-tester. Kilde: Nvidia Nvidia<\/figcaption><\/figure>\n

I 2018 sa Jensen Huang, administrerende direkt\u00f8r i Nvidia, at ytelsen til GPU-er vil mer enn dobles hvert annet \u00e5r. Denne p\u00e5standen ble kalt Haungs lov, og den har vist seg \u00e5 v\u00e6re sann, ettersom den etterlater Moores lov er i ferd med \u00e5 forsvinne<\/a> i databehandlingens bakspeil.<\/p>\n

Hva s\u00e5?<\/h2>\n

MLPerf-referansetreningstesten som Nvidia klarte, bruker bare en del av hele datasettet som GPT-3 ble trent p\u00e5. Hvis du tar tiden Eos satte i MLPerf-testen og ekstrapolerer for hele GPT-3-datasettet, kan den trene hele modellen p\u00e5 bare 8 dager.<\/p>\n

Hvis du hadde fors\u00f8kt \u00e5 gj\u00f8re det samme med det tidligere toppmoderne systemet, som besto av 512 A100 GPU-er, ville det tatt rundt 170 dager.<\/p>\n

Hvis du skulle l\u00e6re opp en ny AI-modell, kan du tenke deg forskjellen i tid til markedet og kostnader som 8 dager versus 170 dager utgj\u00f8r?<\/p>\n

H100-GPU-ene er ikke bare mye kraftigere enn A100-GPU-ene, de er ogs\u00e5 opptil 3,5 ganger mer energieffektive. Energiforbruk og AIs karbonfotavtrykk<\/a> er reelle problemer som m\u00e5 l\u00f8ses.<\/p>\n

For \u00e5 f\u00e5 et inntrykk av hvor raskt AI-prosesseringen blir bedre, kan du tenke p\u00e5 ChatGPT, som ble satt i drift for litt under ett \u00e5r siden. Den underliggende modellen, GPT-3, ble trent opp p\u00e5 10 240 Nvidia V100 GPU-er.<\/p>\n

Mindre enn ett \u00e5r senere har Eos 28 ganger s\u00e5 stor prosessorkraft som det oppsettet, med en 3,5 ganger s\u00e5 stor effektivitetsforbedring.<\/p>\n

\"\"
Prosessorkraft som trente GPT-3 vs Nvidia Eos<\/figcaption><\/figure>\n

Da OpenAIs Sam Altman avsluttet den siste DevDay, sa han at prosjektene OpenAI jobbet med, ville f\u00e5 de siste utgivelsene til \u00e5 se gammeldagse ut.<\/p>\n

Med tanke p\u00e5 spranget i prosessorkraft som selskaper som Nvidia oppn\u00e5r, oppsummerer Altmans p\u00e5stand sannsynligvis fremtiden for AI-industrien som helhet.<\/p>","protected":false},"excerpt":{"rendered":"

Nvidia skalerte og optimaliserte AI-superdatamaskinen Eos for \u00e5 sette nye rekorder i MLPerf AI-trening. Da Nvidia slo p\u00e5 AI-superdatamaskinen Eos i mai, ble 10 752 NVIDIA H100 Tensor Core GPU-er vekket til live, og de har nettopp blitt testet i den virkelige verden. Denne enest\u00e5ende prosessorkraften, sammen med nye programvareoptimaliseringer, har gjort Eos i stand til \u00e5 presse MLPerf-referansen til rekordh\u00f8ye niv\u00e5er. MLPerf-referansen med \u00e5pen kildekode er et sett med trenings- og inferenstester som er utviklet for \u00e5 m\u00e5le ytelsen til maskinl\u00e6ringsarbeidsbelastninger p\u00e5 virkelige datasett. Et av de fremtredende resultatene var at Eos var i stand til \u00e5<\/p>","protected":false},"author":6,"featured_media":973,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[106],"class_list":["post-7271","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-nvidia"],"yoast_head":"\nNvidia sets new AI training records in MLPerf benchmarks | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/nb\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Nvidia sets new AI training records in MLPerf benchmarks | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Nvidia scaled and optimized its Eos AI supercomputer to set new MLPerf AI training benchmark records. When Nvidia switched its AI supercomputer Eos on in May, 10,752 NVIDIA H100 Tensor Core GPUs came to life and they’ve just been put through some real-world test trials. This unprecedented processing power along with new software optimizations has enabled Eos to push the MLPerf benchmark into record territory. The open-source MLPerf benchmark is a set of training and inference tests designed to measure the performance of machine learning workloads on real-world datasets. 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