{"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\/pt\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/","title":{"rendered":"A Nvidia estabelece novos recordes de treino de IA nos par\u00e2metros de refer\u00eancia MLPerf"},"content":{"rendered":"

A Nvidia dimensionou e optimizou o seu supercomputador Eos AI para estabelecer novos recordes de benchmark de forma\u00e7\u00e3o MLPerf AI.<\/strong><\/p>\n

Quando a Nvidia ligou o seu supercomputador de IA Eos em maio, 10.752 GPUs NVIDIA H100 Tensor Core ganharam vida e acabaram de ser submetidas a alguns testes reais.<\/p>\n

Este poder de processamento sem precedentes, juntamente com as novas optimiza\u00e7\u00f5es de software, permitiu \u00e0 Eos impulsionar o MLPerf refer\u00eancia<\/a> em territ\u00f3rio recorde.<\/p>\n

O benchmark MLPerf de c\u00f3digo aberto \u00e9 um conjunto de testes de forma\u00e7\u00e3o e infer\u00eancia concebido para medir o desempenho de cargas de trabalho de aprendizagem autom\u00e1tica em conjuntos de dados do mundo real.<\/p>\n

Um dos resultados de destaque foi o facto de o Eos ter conseguido treinar um modelo GPT-3 com 175 mil milh\u00f5es de par\u00e2metros em mil milh\u00f5es de fichas em apenas 3,9 minutos.<\/p>\n

Quando a Nvidia estabeleceu o recorde neste par\u00e2metro de compara\u00e7\u00e3o h\u00e1 menos de 6 meses, demorou quase 3 vezes mais tempo, com um tempo de 10,9 minutos.<\/p>\n

A Nvidia tamb\u00e9m conseguiu atingir uma taxa de efici\u00eancia de 93% durante os testes, o que significa que utilizou quase todo o poder de computa\u00e7\u00e3o teoricamente dispon\u00edvel no Eos.<\/p>\n

O Microsoft Azure, que utiliza praticamente a mesma configura\u00e7\u00e3o H100 que o Eos na sua m\u00e1quina virtual ND H100 v5, ficou a 2% dos resultados dos testes da Nvidia nos seus testes MLPerf.<\/p>\n

\"\"
Hardware da Nvidia utilizado nos testes MLPerf. Fonte: Nvidia<\/figcaption><\/figure>\n

Em 2018, Jensen Huang, CEO da Nvidia, afirmou que o desempenho das GPUs mais do que duplicar\u00e1 a cada dois anos. Essa afirma\u00e7\u00e3o foi cunhada como Lei de Haung e provou ser verdadeira, pois deixa A Lei de Moore est\u00e1 a desaparecer<\/a> no espelho retrovisor da computa\u00e7\u00e3o.<\/p>\n

E depois?<\/h2>\n

O teste de treino de refer\u00eancia MLPerf que a Nvidia realizou utiliza apenas uma parte do conjunto de dados completo em que o GPT-3 foi treinado. Se pegarmos no tempo que o Eos estabeleceu no teste MLPerf e extrapolarmos para o conjunto de dados GPT-3 completo, ent\u00e3o poderia treinar o modelo completo em apenas 8 dias.<\/p>\n

Se tentasse fazer isso utilizando o seu anterior sistema de \u00faltima gera\u00e7\u00e3o, composto por 512 GPUs A100, demoraria cerca de 170 dias.<\/p>\n

Se estivesse a treinar um novo modelo de IA, consegue imaginar a diferen\u00e7a no tempo de coloca\u00e7\u00e3o no mercado e no custo que representam 8 dias versus 170 dias?<\/p>\n

As GPUs H100 n\u00e3o s\u00f3 s\u00e3o muito mais potentes do que as GPUs A100, como tamb\u00e9m s\u00e3o at\u00e9 3,5 vezes mais eficientes em termos energ\u00e9ticos. Utiliza\u00e7\u00e3o de energia e A pegada de carbono da IA<\/a> s\u00e3o quest\u00f5es reais que precisam de ser resolvidas.<\/p>\n

Para ter uma ideia da rapidez com que o processamento de IA est\u00e1 a melhorar, pense no ChatGPT, que foi lan\u00e7ado h\u00e1 pouco menos de um ano. O modelo subjacente, GPT-3, foi treinado em 10.240 GPUs Nvidia V100.<\/p>\n

Menos de um ano depois, o Eos tem 28 vezes mais capacidade de processamento do que essa configura\u00e7\u00e3o, com uma melhoria de 3,5 vezes na efici\u00eancia.<\/p>\n

\"\"
Pot\u00eancia de processamento que treinou GPT-3 vs Nvidia Eos<\/figcaption><\/figure>\n

Quando Sam Altman, da OpenAI, concluiu o recente DevDay, disse que os projectos em que a OpenAI estava a trabalhar fariam com que os seus \u00faltimos lan\u00e7amentos parecessem estranhos.<\/p>\n

Considerando o salto no poder de processamento que empresas como a Nvidia est\u00e3o a conseguir, a afirma\u00e7\u00e3o de Altman resume provavelmente o futuro da ind\u00fastria da IA como um todo.<\/p>","protected":false},"excerpt":{"rendered":"

A Nvidia dimensionou e optimizou o seu supercomputador de IA Eos para estabelecer novos recordes de benchmark de forma\u00e7\u00e3o de IA MLPerf. Quando a Nvidia ligou o seu supercomputador de IA Eos em maio, 10.752 GPUs NVIDIA H100 Tensor Core ganharam vida e acabaram de ser submetidas a alguns testes no mundo real. Este poder de processamento sem precedentes, juntamente com as novas optimiza\u00e7\u00f5es de software, permitiu ao Eos levar o benchmark MLPerf para territ\u00f3rio recorde. O benchmark MLPerf de c\u00f3digo aberto \u00e9 um conjunto de testes de forma\u00e7\u00e3o e infer\u00eancia concebido para medir o desempenho de cargas de trabalho de aprendizagem autom\u00e1tica em conjuntos de dados do mundo real. Um dos resultados de destaque foi o facto de o Eos ter conseguido<\/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\/pt\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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\/pt\/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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