{"id":2095,"date":"2023-06-27T20:52:15","date_gmt":"2023-06-27T20:52:15","guid":{"rendered":"https:\/\/dailyai.com\/?p=2095"},"modified":"2023-07-04T11:31:32","modified_gmt":"2023-07-04T11:31:32","slug":"nvidias-h100-chips-smash-ai-training-and-deployment-records-in-benchmark-test","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2023\/06\/nvidias-h100-chips-smash-ai-training-and-deployment-records-in-benchmark-test\/","title":{"rendered":"Nvidias H100-chips smadrer AI-tr\u00e6ningsrekorder i benchmark-test"},"content":{"rendered":"<p><b>NVIDIA's nye H100 GPU'er har sat nye benchmarks for AI-tr\u00e6ningsopgaver og sl\u00e5et flere rekorder i processen.\u00a0<\/b><\/p>\n<p><span style=\"font-weight: 400\">MLPerf blev grundlagt af et konsortium af forskere, akademikere og andre specialister, der byggede benchmarks for at teste, hvor hurtigt systemer kan implementere og k\u00f8re AI-modeller. I bund og grund er MLPerf en r\u00e6kke tests, der er designet til at m\u00e5le hastigheden og effektiviteten af hardware, software og tjenester til maskinl\u00e6ring (ML).\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Nvidia, verdens f\u00f8rende inden for AI-hardware, testede en <\/span><a href=\"https:\/\/blogs.nvidia.com\/blog\/2023\/06\/27\/generative-ai-debut-mlperf\/\"><span style=\"font-weight: 400\">klynge af 3.584 H100 GPU'er<\/span><\/a><span style=\"font-weight: 400\"> for at vise deres formidable hurtighed.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Klyngen, der er udviklet i f\u00e6llesskab af AI-startup'en Inflection AI og administreres af CoreWeave, en cloud-tjenesteudbyder med speciale i GPU-baserede arbejdsbelastninger, gennemf\u00f8rte et tr\u00e6ningsbenchmark baseret p\u00e5 GPT-3-modellen p\u00e5 mindre end 11 minutter.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Med andre ord tr\u00e6nede klyngen en GPT-3-\u00e6kvivalent model med omkring 175 mia. parametre p\u00e5 omtrent samme tid, som det tager at brygge en kop kaffe eller g\u00e5 tur med hunden. Vi ved ikke, hvor lang tid det tog OpenAI at tr\u00e6ne GPT-3, men det var i hvert fald ikke 11 minutter.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">H100 GPU'erne satte rekorder p\u00e5 tv\u00e6rs af 8 andre MLPerf-tests og viste deres r\u00e5styrke og alsidighed. Her er nogle af resultaterne:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><b>Stor sprogmodel (GPT-3)<\/b><span style=\"font-weight: 400\">: 10,9 minutter<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Behandling af naturligt sprog (BERT)<\/b><span style=\"font-weight: 400\">: 0,13 minutter (8 sekunder)<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Anbefaling (DLRMv2)<\/b><span style=\"font-weight: 400\">: 1.61 minutter<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Objektdetektering, sv\u00e6rv\u00e6gt (maske R-CNN)<\/b><span style=\"font-weight: 400\">: 1.47 minutter<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Objektregistrering, letv\u00e6gts (RetinaNet)<\/b><span style=\"font-weight: 400\">: 1.51 minutter<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Klassificering af billeder (ResNet-50 v1.5)<\/b><span style=\"font-weight: 400\">: 0,18 minutter (11 sekunder)<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Billedsegmentering (3D U-Net)<\/b><span style=\"font-weight: 400\">: 0,82 minutter (49 sekunder)<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Talegenkendelse (RNN-T)<\/b><span style=\"font-weight: 400\">: 1.65 minutter<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">I deres seneste runde af benchmarking, kaldet v3.0, opdaterede MLPerf ogs\u00e5 sin test for anbefalingssystemer, som er algoritmer, der foresl\u00e5r produkter eller tjenester til brugere baseret p\u00e5 deres tidligere adf\u00e6rd.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Den nye test bruger et st\u00f8rre datas\u00e6t og en mere aktuel AI-model for bedre at kunne efterligne de udfordringer, som tjenesteudbydere st\u00e5r over for. Nvidia er den eneste virksomhed, der har indsendt resultater p\u00e5 dette benchmark.<\/span><\/p>\n<h2><span style=\"font-weight: 400\">MLPerf-benchmarking<\/span><\/h2>\n<p><a href=\"https:\/\/mlcommons.org\/en\/\"><span style=\"font-weight: 400\">MLCommons<\/span><\/a><span style=\"font-weight: 400\">et AI- og teknologikonsortium, offentliggjorde for nylig de nyeste resultater fra deres AI-benchmarking-tests.<\/span><\/p>\n<p><span style=\"font-weight: 400\"> Den prim\u00e6re benchmarkrunde blev kaldt v3.0, som vurderer effektiviteten af tr\u00e6ning af maskinl\u00e6ringsmodeller. En anden runde, kaldet Tiny v1.1, unders\u00f8ger ML-applikationer til ultrakompakte enheder med lavt str\u00f8mforbrug.<\/span><\/p>\n<p><span style=\"font-weight: 400\">MLPerf-runden v3.0 havde deltagelse af virksomheder som ASUSTek, Azure, Dell, Fujitsu, GIGABYTE, H3C, IEI, Intel &amp; Habana Labs, Krai, Lenovo, NVIDIA, NVIDIA + CoreWeave, Quanta Cloud Technology, Supermicro og xFusion.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Samlet set viste modellerne pr\u00e6stationsforbedringer p\u00e5 op til 1,54 gange i l\u00f8bet af de sidste 6 m\u00e5neder eller 33 til 49 gange siden f\u00f8rste runde, <\/span><a href=\"https:\/\/mlcommons.org\/en\/inference-datacenter-05\/\"><span style=\"font-weight: 400\">v0.5<\/span><\/a><span style=\"font-weight: 400\">i 2019, hvilket illustrerer tempoet i udviklingen af maskinl\u00e6ringssystemer.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Nvidia vandt runden v3.0 takket v\u00e6re deres ultra-high-end H100-chips, som de sandsynligvis vil beholde i en overskuelig fremtid. <\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>NVIDIA's nye H100 GPU'er har sat nye benchmarks for AI-tr\u00e6ningsopgaver og sl\u00e5et flere rekorder i processen.  MLPerf blev grundlagt af et konsortium af forskere, akademikere og andre specialister, der byggede benchmarks for at teste, hvor hurtigt systemer kan implementere og k\u00f8re AI-modeller. I bund og grund er MLPerf en r\u00e6kke tests, der er designet til at m\u00e5le hastigheden og effektiviteten af hardware, software og tjenester til maskinl\u00e6ring (ML).  Nvidia, verdens f\u00f8rende inden for AI-hardware, testede en klynge af 3.584 H100 GPU'er for at vise deres formidable hastighed.  Klyngen, der er udviklet af AI-startup'en Inflection AI og administreret af CoreWeave, en cloud<\/p>","protected":false},"author":2,"featured_media":2096,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[173,106,100],"class_list":["post-2095","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-h100","tag-nvidia","tag-semiconductors"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Nvidia&#039;s H100 chips smash AI training records in benchmark test | DailyAI<\/title>\n<meta name=\"description\" content=\"NVIDIA&#039;s new H100 GPUs have set new benchmarks for AI training tasks, breaking several records in the process.\u00a0\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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