{"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\/de\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/","title":{"rendered":"Nvidia stellt neue KI-Trainingsrekorde in MLPerf-Benchmarks auf"},"content":{"rendered":"

Nvidia hat seinen KI-Supercomputer Eos skaliert und optimiert, um neue MLPerf-Benchmark-Rekorde beim KI-Training aufzustellen.<\/strong><\/p>\n

Als Nvidia im Mai seinen KI-Supercomputer Eos einschaltete, wurden 10.752 NVIDIA H100 Tensor Core GPUs zum Leben erweckt, die gerade einige Praxistests durchlaufen haben.<\/p>\n

Diese beispiellose Verarbeitungsleistung zusammen mit neuen Software-Optimierungen hat es Eos erm\u00f6glicht, den MLPerf Benchmark<\/a> auf Rekordniveau.<\/p>\n

Der quelloffene MLPerf-Benchmark ist eine Reihe von Trainings- und Inferenztests, mit denen die Leistung von maschinellen Lernprozessen auf realen Datens\u00e4tzen gemessen werden kann.<\/p>\n

Eines der herausragenden Ergebnisse war, dass Eos ein GPT-3-Modell mit 175 Milliarden Parametern auf einer Milliarde Token in nur 3,9 Minuten trainieren konnte.<\/p>\n

Als Nvidia vor weniger als 6 Monaten den Rekord bei diesem Benchmark aufstellte, dauerte es mit 10,9 Minuten fast dreimal so lange.<\/p>\n

Nvidia konnte bei den Tests au\u00dferdem eine Effizienzrate von 93% erreichen, was bedeutet, dass fast die gesamte theoretisch in Eos verf\u00fcgbare Rechenleistung genutzt wurde.<\/p>\n

Microsoft Azure, das in seiner virtuellen Maschine ND H100 v5 ein \u00e4hnliches H100-Setup wie Eos verwendet, kam in seinen MLPerf-Tests bis auf 2% an die Testergebnisse von Nvidia heran.<\/p>\n

\"\"
In den MLPerf-Tests verwendete Nvidia-Hardware. Quelle: Nvidia<\/figcaption><\/figure>\n

Im Jahr 2018 sagte Jensen Huang, der CEO von Nvidia, dass sich die Leistung von Grafikprozessoren alle zwei Jahre mehr als verdoppeln wird. Diese Behauptung wurde als Haung's Law bezeichnet und hat sich bewahrheitet, denn sie l\u00e4sst Moore's Law verschwindet<\/a> in den R\u00fcckspiegeln der Computer.<\/p>\n

Na und?<\/h2>\n

Der MLPerf-Benchmark-Trainingstest, den Nvidia erfolgreich absolviert hat, verwendet nur einen Teil des vollst\u00e4ndigen Datensatzes, auf dem GPT-3 trainiert wurde. Nimmt man die Zeit, die Eos im MLPerf-Test angesetzt hat, und extrapoliert sie f\u00fcr den gesamten GPT-3-Datensatz, dann k\u00f6nnte das gesamte Modell in nur 8 Tagen trainiert werden.<\/p>\n

W\u00fcrde man dies mit dem bisherigen, hochmodernen System aus 512 A100-GPUs versuchen, w\u00fcrde es rund 170 Tage dauern.<\/p>\n

Wenn Sie ein neues KI-Modell trainieren, k\u00f6nnen Sie sich den Unterschied in der Markteinf\u00fchrungszeit und den Kosten vorstellen, den 8 Tage gegen\u00fcber 170 Tagen ausmachen?<\/p>\n

Die H100-GPUs sind nicht nur viel leistungsf\u00e4higer als die A100-GPUs, sondern auch bis zu 3,5 Mal energieeffizienter. Energienutzung und Der Kohlenstoff-Fu\u00dfabdruck von AI<\/a> sind echte Probleme, die angegangen werden m\u00fcssen.<\/p>\n

Um eine Vorstellung davon zu bekommen, wie schnell sich die KI-Verarbeitung verbessert, denken Sie an ChatGPT, das vor knapp einem Jahr in Betrieb ging. Das zugrunde liegende Modell, GPT-3, wurde auf 10.240 Nvidia V100 GPUs trainiert.<\/p>\n

Weniger als ein Jahr sp\u00e4ter verf\u00fcgt der Eos \u00fcber die 28-fache Rechenleistung bei einer 3,5-fachen Verbesserung der Effizienz.<\/p>\n

\"\"
Rechenleistung, die GPT-3 gegen\u00fcber Nvidia Eos trainiert<\/figcaption><\/figure>\n

Als Sam Altman von OpenAI den letzten DevDay beendete, sagte er, dass die Projekte, an denen OpenAI arbeitet, die letzten Versionen alt aussehen lassen w\u00fcrden.<\/p>\n

In Anbetracht der sprunghaften Zunahme der Rechenleistung, die Unternehmen wie Nvidia erzielen, d\u00fcrfte Altmans Aussage die Zukunft der KI-Branche insgesamt widerspiegeln.<\/p>","protected":false},"excerpt":{"rendered":"

Nvidia hat seinen KI-Supercomputer Eos skaliert und optimiert, um neue MLPerf-Benchmark-Rekorde beim KI-Training aufzustellen. Als Nvidia im Mai seinen KI-Supercomputer Eos einschaltete, wurden 10.752 NVIDIA H100 Tensor Core-GPUs zum Leben erweckt, die gerade einige Praxistests absolviert haben. Dank dieser beispiellosen Rechenleistung und neuer Software-Optimierungen konnte Eos den MLPerf-Benchmark auf ein Rekordniveau heben. Der Open-Source-Benchmark MLPerf besteht aus einer Reihe von Trainings- und Inferenztests, mit denen die Leistung von Workloads f\u00fcr maschinelles Lernen auf realen Datens\u00e4tzen gemessen werden soll. Eines der herausragenden Ergebnisse war, dass Eos in der Lage war<\/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\/de\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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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