{"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\/es\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/","title":{"rendered":"Nvidia establece nuevos r\u00e9cords de entrenamiento de IA en las pruebas MLPerf"},"content":{"rendered":"

Nvidia ha escalado y optimizado su superordenador de IA Eos para establecer nuevos r\u00e9cords de referencia de entrenamiento de IA MLPerf.<\/strong><\/p>\n

Cuando Nvidia encendi\u00f3 su superordenador de IA Eos en mayo, 10.752 GPUs NVIDIA H100 Tensor Core cobraron vida y acaban de someterse a algunas pruebas en el mundo real.<\/p>\n

Esta potencia de procesamiento sin precedentes, junto con las nuevas optimizaciones de software, ha permitido a Eos impulsar el MLPerf referencia<\/a> en territorio r\u00e9cord.<\/p>\n

El banco de pruebas de c\u00f3digo abierto MLPerf es un conjunto de pruebas de entrenamiento e inferencia dise\u00f1ado para medir el rendimiento de las cargas de trabajo de aprendizaje autom\u00e1tico en conjuntos de datos del mundo real.<\/p>\n

Uno de los resultados m\u00e1s destacados fue que Eos fue capaz de entrenar un modelo GPT-3 con 175.000 millones de par\u00e1metros en mil millones de fichas en s\u00f3lo 3,9 minutos.<\/p>\n

Cuando Nvidia estableci\u00f3 el r\u00e9cord en esta prueba hace menos de 6 meses, tard\u00f3 casi 3 veces m\u00e1s, con un tiempo de 10,9 minutos.<\/p>\n

Nvidia tambi\u00e9n fue capaz de alcanzar un \u00edndice de eficiencia de 93% durante las pruebas, lo que significa que utiliz\u00f3 casi toda la potencia de c\u00e1lculo te\u00f3ricamente disponible en Eos.<\/p>\n

Microsoft Azure, que utiliza pr\u00e1cticamente la misma configuraci\u00f3n H100 que Eos en su m\u00e1quina virtual ND H100 v5, se qued\u00f3 a 2% de los resultados de Nvidia en sus pruebas MLPerf.<\/p>\n

\"\"
Hardware de Nvidia utilizado en las pruebas MLPerf. Fuente: Nvidia<\/figcaption><\/figure>\n

En 2018 Jensen Huang, CEO de Nvidia, afirm\u00f3 que el rendimiento de las GPU se duplicar\u00e1 con creces cada dos a\u00f1os. Esta afirmaci\u00f3n fue acu\u00f1ada como la Ley de Haung y ha demostrado ser cierta ya que deja La Ley de Moore desaparece<\/a> en el retrovisor inform\u00e1tico.<\/p>\n

\u00bfY qu\u00e9?<\/h2>\n

La prueba de entrenamiento MLPerf que super\u00f3 Nvidia utiliza s\u00f3lo una parte del conjunto de datos completo con el que se entren\u00f3 GPT-3. Si se toma el tiempo establecido por Eos en la prueba MLPerf y se extrapola al conjunto de datos completo de GPT-3, se podr\u00eda entrenar el modelo completo en tan solo 8 d\u00edas.<\/p>\n

Si se intentara hacerlo con su anterior sistema de \u00faltima generaci\u00f3n compuesto por 512 GPU A100, se tardar\u00eda unos 170 d\u00edas.<\/p>\n

Si estuvieras entrenando un nuevo modelo de IA, \u00bfte imaginas la diferencia en tiempo de comercializaci\u00f3n y coste que representan 8 d\u00edas frente a 170 d\u00edas?<\/p>\n

Las GPU H100 no s\u00f3lo son mucho m\u00e1s potentes que las GPU A100, sino que adem\u00e1s son hasta 3,5 veces m\u00e1s eficientes desde el punto de vista energ\u00e9tico. Uso de la energ\u00eda y La huella de carbono de la IA<\/a> son problemas reales que hay que abordar.<\/p>\n

Para hacernos una idea de lo r\u00e1pido que est\u00e1 mejorando el procesamiento de la IA, pensemos en ChatGPT, que se puso en marcha hace poco menos de un a\u00f1o. El modelo subyacente, GPT-3, se entren\u00f3 en 10.240 GPU Nvidia V100.<\/p>\n

Menos de un a\u00f1o despu\u00e9s, Eos tiene 28 veces la potencia de procesamiento de esa configuraci\u00f3n con una mejora de la eficiencia de 3,5 veces.<\/p>\n

\"\"
Potencia de procesamiento que entren\u00f3 GPT-3 vs Nvidia Eos<\/figcaption><\/figure>\n

Cuando Sam Altman de OpenAI concluy\u00f3 el reciente DevDay dijo que los proyectos en los que OpenAI estaba trabajando har\u00edan que sus \u00faltimas versiones parecieran pintorescas.<\/p>\n

Teniendo en cuenta el salto en potencia de procesamiento que est\u00e1n logrando empresas como Nvidia, la afirmaci\u00f3n de Altman probablemente resume el futuro de la industria de la IA en su conjunto.<\/p>","protected":false},"excerpt":{"rendered":"

Nvidia ha escalado y optimizado su superordenador de IA Eos para establecer nuevos r\u00e9cords de entrenamiento de IA MLPerf. Cuando Nvidia encendi\u00f3 su superordenador de IA Eos en mayo, 10.752 GPUs NVIDIA H100 Tensor Core cobraron vida y acaban de someterse a algunas pruebas en el mundo real. Esta potencia de procesamiento sin precedentes, unida a las nuevas optimizaciones de software, ha permitido a Eos situar el \u00edndice de referencia MLPerf en territorio r\u00e9cord. La prueba MLPerf de c\u00f3digo abierto es un conjunto de pruebas de entrenamiento e inferencia dise\u00f1adas para medir el rendimiento de las cargas de trabajo de aprendizaje autom\u00e1tico en conjuntos de datos reales. Uno de los resultados m\u00e1s destacados fue que Eos fue capaz de<\/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\/es\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\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\/es\/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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