[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fuGcyTBu_RMyAGjlnVKQ8aAxwJA_zWS_u3Lz1UaKi8w8":3},{"tableOfContents":4,"markDownContent":5,"htmlContent":6,"metaTitle":7,"metaDescription":8,"wordCount":9,"readTime":10,"title":7,"nbDownloads":11,"excerpt":12,"lang":13,"url":14,"intro":12,"featured":4,"state":15,"author":16,"authorId":17,"datePublication":21,"dateCreation":22,"dateUpdate":23,"mainCategory":24,"categories":44,"metaDatas":50,"imageUrl":12,"imageThumbUrls":51,"id":52},false,"A General-purpose AI(GPAI) model is defined by [Article 3(63) of the AI Act.](https://artificialintelligenceact.eu/article/3/)\r\n\r\nIt is the **amount of computational resources** that are used to train the model measured in FLOP, **and the modalities** of the model that will define whether a model is a GPAI or not.\r\n\r\n> A model is likely to be considered a GPAI if:\r\n>\r\n> - **The training compute of the model is greater than 10^23 FLOP.** As explained in the Guidelines, the \"*amount of compute used to train a model is typically proportional to the number obtained by multiplyiing the number of its parameters with the number of its trainig examples\".*\r\n> - **It is capable of performing a wide range of distinct tasks** like generate language (text or audio), text-to-image or text-to-video. **The model's training on a broad range of natural language, ability to use language to communicative, store knowledge & reason is an indicator of its significant generality.**\r\n>\r\n> If the first threshold is met, but the model cannot perform a wide range of distinct tasks, then it is not a GPAI.\r\n\r\n\\*> *For example, if it uses 10^24 FLOP but can only transcribe speech to text, it is not a GPAI because it can perform a narrow set of tasks.*\r\n\r\n> If the model **is general enough in its capabilities without meeting the threshold, it is still a GPAI.**\r\n\r\nWhile this single threshold seems easier to hold as a criterion, **it is not set in stone,** as the European Commission indicates that it continues to investigate other criteria.","\u003Cp>A General-purpose AI(GPAI) model is defined by \u003Ca href=\"https://artificialintelligenceact.eu/article/3/\" rel=\"nofollow\">Article 3(63) of the AI Act.\u003C/a>\u003C/p>\r\n\u003Cp>It is the \u003Cstrong>amount of computational resources\u003C/strong> that are used to train the model measured in FLOP, \u003Cstrong>and the modalities\u003C/strong> of the model that will define whether a model is a GPAI or not.\u003C/p>\r\n\u003Cblockquote>\r\n\u003Cp>A model is likely to be considered a GPAI if:\u003C/p>\r\n\u003Cul>\r\n\u003Cli>\u003Cstrong>The training compute of the model is greater than 10^23 FLOP.\u003C/strong> As explained in the Guidelines, the \"\u003Cem>amount of compute used to train a model is typically proportional to the number obtained by multiplyiing the number of its parameters with the number of its trainig examples\".\u003C/em>\u003C/li>\r\n\u003Cli>\u003Cstrong>It is capable of performing a wide range of distinct tasks\u003C/strong> like generate language (text or audio), text-to-image or text-to-video. \u003Cstrong>The model's training on a broad range of natural language, ability to use language to communicative, store knowledge & reason is an indicator of its significant generality.\u003C/strong>\u003C/li>\r\n\u003C/ul>\r\n\u003Cp>If the first threshold is met, but the model cannot perform a wide range of distinct tasks, then it is not a GPAI.\u003C/p>\r\n\u003C/blockquote>\r\n\u003Cp>*> \u003Cem>For example, if it uses 10^24 FLOP but can only transcribe speech to text, it is not a GPAI because it can perform a narrow set of tasks.\u003C/em>\u003C/p>\r\n\u003Cblockquote>\r\n\u003Cp>If the model \u003Cstrong>is general enough in its capabilities without meeting the threshold, it is still a GPAI.\u003C/strong>\u003C/p>\r\n\u003C/blockquote>\r\n\u003Cp>While this single threshold seems easier to hold as a criterion, \u003Cstrong>it is not set in stone,\u003C/strong> as the European Commission indicates that it continues to investigate other criteria.\u003C/p>\r\n","General-purpose AI (GPAI) model ","It is the amount of computational resources that are used to train the model measured in FLOP, and the modalities of the model that will define whether a model ",260,2,0,null,"en","general-purpose-ai-gpai-model","Published",{"id":17,"displayName":18,"avatarUrl":19,"bio":12,"blogUrl":12,"color":12,"userId":17,"creationDate":20},20352,"Leïla Sayssa","https://static.dastra.eu/tenant-3/avatar/20352/TDYeY3C8Rz1lLE/dpo-avatar-h01-150.png","2025-03-03T11:08:22","2025-07-24T09:01:00","2025-07-24T09:01:54.9563196","2025-08-29T09:21:23.3476242",{"id":25,"name":26,"description":27,"url":28,"color":29,"parentId":12,"count":12,"imageUrl":30,"parent":12,"order":11,"translations":31},21,"Glossary","Definition of every word used by Dastra","glossary","#643bb0","https://static.dastra.eu/tag/b308b9d3-37af-4e92-8354-ab8adec1740a/documentation-1000.png",[32,36,40],{"lang":33,"name":34,"description":35},"fr","Glossaire","La définition de tous les termes utilisés dans Dastra",{"lang":37,"name":38,"description":39},"es","Glosario","La definición de todos los términos utilizados en Dastra",{"lang":41,"name":42,"description":43},"de","Glossar","Die Definition aller in Dastra verwendeten Begriffe",[45],{"id":25,"name":26,"description":27,"url":28,"color":29,"parentId":12,"count":12,"imageUrl":30,"parent":12,"order":11,"translations":46},[47,48,49],{"lang":33,"name":34,"description":35},{"lang":37,"name":38,"description":39},{"lang":41,"name":42,"description":43},[],[],59460]