{"id":8047,"date":"2023-12-06T12:34:54","date_gmt":"2023-12-06T12:34:54","guid":{"rendered":"https:\/\/dailyai.com\/?p=8047"},"modified":"2023-12-06T12:34:54","modified_gmt":"2023-12-06T12:34:54","slug":"new-approach-could-make-large-language-models-300x-faster","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2023\/12\/new-approach-could-make-large-language-models-300x-faster\/","title":{"rendered":"Ny metode kan gj\u00f8re store spr\u00e5kmodeller 300 ganger raskere"},"content":{"rendered":"<p><strong>Forskere fra ETH Z\u00fcrich har funnet ut at store spr\u00e5kmodeller (Large Language Models, LLM) bare trenger \u00e5 bruke en liten del av nevronene sine til individuelle slutninger. Den nye tiln\u00e6rmingen deres lover \u00e5 f\u00e5 LLM-er til \u00e5 kj\u00f8re mye raskere.<\/strong><\/p>\n<p>For \u00e5 forst\u00e5 hvordan de har klart \u00e5 \u00f8ke hastigheten p\u00e5 AI-modeller, m\u00e5 vi f\u00e5 en grov oversikt over noen av de tekniske tingene som utgj\u00f8r en AI-spr\u00e5kmodell.<\/p>\n<p>AI-modeller som GPT eller Llama best\u00e5r av feedforward-nettverk, en type kunstig nevrale nettverk.<\/p>\n<p>Feedforward-nettverk (FF) er vanligvis organisert i lag, der hvert lag med nevroner mottar input fra det forrige laget og sender output til neste lag.<\/p>\n<p>Dette inneb\u00e6rer tett matrisemultiplikasjon (DMM), som krever at hvert nevron i FF utf\u00f8rer beregninger p\u00e5 alle inndataene fra det forrige laget. Og dette er grunnen til at <a href=\"https:\/\/dailyai.com\/nb\/2023\/11\/nvidia-achieves-record-18b-q3-revenue-crediting-generative-ai\/\">Nvidia selger s\u00e5 mange av sine GPU-er<\/a> fordi denne prosessen krever mye prosessorkraft.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2311.10770.pdf\" target=\"_blank\" rel=\"noopener\">Forskerne<\/a> brukte Fast Feedforward Networks (FFF) for \u00e5 gj\u00f8re denne prosessen mye raskere. Et FFF tar hvert lag med nevroner, deler det opp i blokker og velger deretter bare de mest relevante blokkene basert p\u00e5 inndataene. Denne prosessen tilsvarer \u00e5 utf\u00f8re betinget matrisemultiplikasjon (CMM).<\/p>\n<p>Det betyr at i stedet for at alle nevronene i et lag er involvert i beregningen, er det bare en sv\u00e6rt liten del som er involvert.<\/p>\n<p>Tenk p\u00e5 det som \u00e5 sortere en haug med post for \u00e5 finne et brev som er ment for deg. I stedet for \u00e5 lese navn og adresse p\u00e5 hvert eneste brev, kan du f\u00f8rst sortere dem etter postnummer og deretter bare fokusere p\u00e5 dem som gjelder ditt omr\u00e5de.<\/p>\n<p>P\u00e5 samme m\u00e5te identifiserer FFF-er bare de f\u00e5 nevronene som kreves for hver beregning, noe som resulterer i bare en br\u00f8kdel av prosesseringen som kreves sammenlignet med tradisjonelle FF-er.<\/p>\n<h2>Hvor mye raskere?<\/h2>\n<p>Forskerne testet metoden sin p\u00e5 en variant av Googles BERT-modell som de kalte UltraFastBERT. UltraFastBERT best\u00e5r av 4095 nevroner, men engasjerer selektivt bare 12 nevroner for hver laginferens.<\/p>\n<p>Dette betyr at UltraFastBERT krever at rundt 0,03% av nevronene er involvert i prosesseringen under inferens, mens vanlig BERT trenger 100% av nevronene for \u00e5 utf\u00f8re beregningen.<\/p>\n<p>I teorien betyr dette at UltraFastBERT vil v\u00e6re 341 ganger raskere enn BERT eller GPT-3.<\/p>\n<p>Hvorfor sier vi \"teoretisk\" n\u00e5r forskerne forsikrer oss om at metoden deres fungerer? Fordi de m\u00e5tte lage en programvarel\u00f8sning for \u00e5 f\u00e5 FFF til \u00e5 fungere med BERT, og bare oppn\u00e5dde en 78 ganger s\u00e5 stor hastighetsforbedring under testing i virkeligheten.<\/p>\n<h2>Det er en hemmelighet<\/h2>\n<p>I forskningsrapporten forklares det at \"tett matrisemultiplikasjon er den mest optimaliserte matematiske operasjonen i databehandlingens historie. Det er lagt ned en enorm innsats i \u00e5 utforme minner, brikker, instruksjonssett og programvarerutiner som kan utf\u00f8re den s\u00e5 raskt som mulig. Mange av disse fremskrittene har v\u00e6rt... hemmeligholdt og kun eksponert for sluttbrukeren gjennom kraftige, men restriktive programmeringsgrensesnitt.\"<\/p>\n<p>I bunn og grunn sier de at ingeni\u00f8rene som har funnet ut de mest effektive m\u00e5tene \u00e5 behandle matematikken som kreves for tradisjonelle FF-nettverk, holder programvaren og algoritmene p\u00e5 lavt niv\u00e5 hemmelig og ikke vil la deg se p\u00e5 koden deres.<\/p>\n<p>Hvis hjernene bak designen av Intels eller Nvidias GPU-er gjorde det mulig \u00e5 f\u00e5 tilgang til lavniv\u00e5kode for \u00e5 implementere FFF-nettverk i AI-modeller, kunne hastighetsforbedringen p\u00e5 341x bli en realitet.<\/p>\n<p>Men vil de gj\u00f8re det? Hvis du kunne konstruere GPU-ene dine slik at folk kunne kj\u00f8pe 99,7% f\u00e6rre av dem for \u00e5 gj\u00f8re samme mengde prosessering, ville du gjort det? \u00d8konomien vil ha noe \u00e5 si her, men FFF-nettverk kan v\u00e6re det neste store spranget innen kunstig intelligens.<\/p>","protected":false},"excerpt":{"rendered":"<p>Forskere fra ETH Z\u00fcrich har funnet ut at store spr\u00e5kmodeller (Large Language Models, LLM) bare trenger \u00e5 bruke en liten del av nevronene sine til individuelle slutninger. Den nye tiln\u00e6rmingen deres lover \u00e5 f\u00e5 LLM-er til \u00e5 kj\u00f8re mye raskere. For \u00e5 forst\u00e5 hvordan de klarte \u00e5 f\u00e5 fart p\u00e5 AI-modellene, m\u00e5 vi f\u00e5 en grov oversikt over noen av de tekniske tingene som utgj\u00f8r en AI-spr\u00e5kmodell. AI-modeller som GPT eller Llama best\u00e5r av feedforward-nettverk, en type kunstige nevrale nettverk. Feedforward-nettverk (FF) er vanligvis organisert i lag, der hvert lag med nevroner mottar input fra<\/p>","protected":false},"author":6,"featured_media":8049,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118,105],"class_list":["post-8047","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms","tag-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>New approach could make large language models 300x faster | 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\/nb\/2023\/12\/new-approach-could-make-large-language-models-300x-faster\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New approach could make large language models 300x faster | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Scientists from ETH Zurich found that Large Language Models (LLM) only need to use a small fraction of their neurons for individual inferences. Their new approach promises to make LLMs run a lot faster. To begin to understand how they managed to speed up AI models we need to get a rough idea of some of the technical stuff that makes up an AI language model. AI models like GPT or Llama are made up of feedforward networks, a type of artificial neural network. Feedforward networks (FF) are typically organized into layers, with each layer of neurons receiving input from\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/nb\/2023\/12\/new-approach-could-make-large-language-models-300x-faster\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2023-12-06T12:34:54+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/12\/neural-network-concept-art.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"625\" \/>\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 name=\"twitter:label1\" 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