{"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\/da\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/","title":{"rendered":"Nvidia s\u00e6tter nye AI-tr\u00e6ningsrekorder i MLPerf-benchmarks"},"content":{"rendered":"

Nvidia skalerede og optimerede sin Eos AI-supercomputer til at s\u00e6tte nye MLPerf AI-tr\u00e6ningsrekorder.<\/strong><\/p>\n

Da Nvidia t\u00e6ndte for sin AI-supercomputer Eos i maj, blev 10.752 NVIDIA H100 Tensor Core GPU'er v\u00e6kket til live, og de er netop blevet testet i den virkelige verden.<\/p>\n

Denne hidtil usete processorkraft sammen med nye softwareoptimeringer har gjort det muligt for Eos at skubbe MLPerf Benchmark<\/a> ind i et rekordomr\u00e5de.<\/p>\n

Open source-benchmarket MLPerf er et s\u00e6t tr\u00e6nings- og inferencetests, der er designet til at m\u00e5le ydeevnen af maskinl\u00e6ringsarbejdsbelastninger p\u00e5 virkelige datas\u00e6t.<\/p>\n

Et af de bem\u00e6rkelsesv\u00e6rdige resultater var, at Eos var i stand til at tr\u00e6ne en GPT-3-model med 175 milliarder parametre p\u00e5 en milliard tokens p\u00e5 bare 3,9 minutter.<\/p>\n

Da Nvidia satte rekorden p\u00e5 dette benchmark for mindre end 6 m\u00e5neder siden, tog det n\u00e6sten 3 gange s\u00e5 lang tid med en tid p\u00e5 10,9 minutter.<\/p>\n

Nvidia var ogs\u00e5 i stand til at opn\u00e5 en effektivitet p\u00e5 93% under testene, hvilket betyder, at den brugte n\u00e6sten al den computerkraft, der teoretisk var til r\u00e5dighed i Eos.<\/p>\n

Microsoft Azure, som bruger stort set den samme H100-ops\u00e6tning som Eos i sin virtuelle ND H100 v5-maskine, kom inden for 2% af Nvidias testresultater i sine MLPerf-tests.<\/p>\n

\"\"
Nvidia-hardware brugt i MLPerf-tests. Kilde: Nvidia<\/figcaption><\/figure>\n

I 2018 sagde Jensen Huang, CEO for Nvidia, at GPU'ernes ydeevne vil blive mere end fordoblet hvert andet \u00e5r. Denne p\u00e5stand blev d\u00f8bt Haungs lov og har vist sig at v\u00e6re sand, da den efterlader Moore's lov forsvinder<\/a> i computerens bakspejl.<\/p>\n

Og hvad s\u00e5?<\/h2>\n

MLPerf-benchmarktr\u00e6ningstesten, som Nvidia klarede, bruger kun en del af det fulde datas\u00e6t, som GPT-3 blev tr\u00e6net p\u00e5. Hvis man tager den tid, Eos satte i MLPerf-testen, og ekstrapolerer til hele GPT-3-datas\u00e6ttet, kunne den tr\u00e6ne hele modellen p\u00e5 bare 8 dage.<\/p>\n

Hvis man fors\u00f8gte at g\u00f8re det med det tidligere state-of-the-art system best\u00e5ende af 512 A100 GPU'er, ville det tage omkring 170 dage.<\/p>\n

Hvis du skulle tr\u00e6ne en ny AI-model, kan du s\u00e5 forestille dig den forskel i time-to-market og omkostninger, som 8 dage i forhold til 170 dage udg\u00f8r?<\/p>\n

H100 GPU'erne er ikke kun meget kraftigere end A100 GPU'erne, de er ogs\u00e5 op til 3,5 gange mere energieffektive. Energiforbrug og AI's CO2-fodaftryk<\/a> er reelle problemer, der skal l\u00f8ses.<\/p>\n

For at f\u00e5 en id\u00e9 om, hvor hurtigt AI-behandling forbedres, kan man t\u00e6nke p\u00e5 ChatGPT, som gik i luften for lidt under et \u00e5r siden. Den underliggende model, GPT-3, blev tr\u00e6net p\u00e5 10.240 Nvidia V100 GPU'er.<\/p>\n

Mindre end et \u00e5r senere har Eos 28 gange s\u00e5 meget processorkraft som det setup med en 3,5 gange st\u00f8rre effektivitet.<\/p>\n

\"\"
Processorkraft, der tr\u00e6nede GPT-3 vs Nvidia Eos<\/figcaption><\/figure>\n

Da OpenAI's Sam Altman afsluttede den seneste DevDay, sagde han, at de projekter, OpenAI arbejdede p\u00e5, ville f\u00e5 de seneste udgivelser til at se gammeldags ud.<\/p>\n

I betragtning af det spring i processorkraft, som virksomheder som Nvidia opn\u00e5r, opsummerer Altmans p\u00e5stand sandsynligvis fremtiden for AI-industrien som helhed.<\/p>","protected":false},"excerpt":{"rendered":"

Nvidia skalerede og optimerede sin Eos AI-supercomputer for at s\u00e6tte nye MLPerf AI-tr\u00e6ningsrekorder. Da Nvidia t\u00e6ndte for sin AI-supercomputer Eos i maj, blev 10.752 NVIDIA H100 Tensor Core GPU'er v\u00e6kket til live, og de er netop blevet testet i den virkelige verden. Denne hidtil usete processorkraft sammen med nye softwareoptimeringer har gjort det muligt for Eos at skubbe MLPerf-benchmarket ind i et rekordomr\u00e5de. Open source MLPerf-benchmarket er et s\u00e6t tr\u00e6nings- og inferencetests, der er designet til at m\u00e5le ydeevnen af maskinl\u00e6ringsarbejdsbelastninger p\u00e5 virkelige datas\u00e6t. Et af de markante resultater var, at Eos var i stand til at<\/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\/da\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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. One of the standout results was that Eos was able to\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/da\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2023-11-10T06:57:38+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-10T07:08:17+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/05\/shutterstock_1742705531.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"667\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Eugene van der Watt\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:site\" content=\"@DailyAIOfficial\" \/>\n<meta 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