{"id":293707,"date":"2025-10-11T04:08:11","date_gmt":"2025-10-11T04:08:11","guid":{"rendered":"https:\/\/www.europesays.com\/us\/293707\/"},"modified":"2025-10-11T04:08:11","modified_gmt":"2025-10-11T04:08:11","slug":"ai-native-applications-reshape-distributed-computing","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/us\/293707\/","title":{"rendered":"AI-native applications reshape distributed computing"},"content":{"rendered":"

AI-native computing is redefining how progress happens \u2014 compressing years of development into days and accelerating the birth of intelligent systems built to learn, adapt and evolve.<\/p>\n

Behind this surge are AI factories: the modern power plants of computation. These purpose-built data centers aren\u2019t just optimizing for performance \u2014 they\u2019re rewriting the rules of scale, energy efficiency and architectural design to support an entirely new generation of self-improving, AI-native applications. As innovation cycles shrink, the question becomes not whether infrastructure can keep up, but how fast it can reinvent itself.<\/p>\n

\"Vipul<\/p>\n

Together Computer\u2019s Vipul Prakash discusses the growth of AI-native applications in relation to AI factories.<\/p>\n

\u201cWhen you had SaaS applications, the ones that were growing really rapidly \u2026 they maybe doubled in nine months,\u201d said Vipul Prakash<\/a> (pictured), co-founder and chief executive officer of Together Computer Inc. \u201cThat was considered to be very fast growth. We are seeing that happen to AI-native applications in nine days. We have customers who are scaling up so rapidly. Their products are so rewarding and are getting distributed internationally. That is creating this \u2026 immense need for AI computation and efficient AI computation.\u201d<\/p>\n

Prakash spoke with theCUBE\u2019s\u00a0John Furrier<\/a>\u00a0at\u00a0theCUBE + NYSE Wired: AI Factories \u2013 Data Centers of the Future<\/a>\u00a0event, during an exclusive broadcast on theCUBE, SiliconANGLE Media\u2019s livestreaming studio. They discussed\u00a0the explosive growth of AI-native applications, the infrastructure required to support them and the emerging role of enterprises in building their own AI factories.<\/p>\n

Defining AI-native applications and infrastructure<\/p>\n

AI-native applications are those where the fundamental functionality of the application is driven by an AI model, according to Prakash. Unlike retrofitted apps that add AI features, these rely on models for their very existence. Examples include ChatGPT, Cursor and Hedra for video generation.<\/p>\n

\u201cThis is different from introducing some AI features in traditional applications because the AI is so central to these applications,\u201d he said. \u201cTheir requirements for efficiency, scale and growth of the underlying AI infrastructure are extreme. They\u2019re really driving the need for building these AI factories rapidly, which consume tokens, to learn from them and then produce them at high throughputs and low latencies.\u201d<\/p>\n

Supporting AI-native applications demands specialized infrastructure \u2014 AI factories \u2014 built for massive throughput and nonstop availability across compute, storage and networking. As enterprises scale their AI initiatives<\/a>, many are turning to open-source models, fine-tuning them with proprietary data to create tailored, high-performing solutions that rival closed-source alternatives, Prakash explained.<\/p>\n

\u201cOnce you have millions of users, you are collecting a lot of data and success criteria for the results that you\u2019re producing,\u201d he said. \u201cAnd that becomes a really great set of data to fine-tune an open-source model on. What we are seeing is that applications are using closed-source APIs, but then they are segmenting their traffic into self-built versions or adapted versions of open-source models, which they\u2019re deploying with Together at scale.\u201d<\/p>\n

One of the biggest infrastructure challenges is data movement. AI systems require data to sit close to computation for rapid access during training, fine-tuning and inference. Together AI addresses this by building large, fabric-connected storage systems adjacent to models, ensuring minimal latency, Prakash noted.<\/p>\n

\u201cWe have embodied systems that are models for robots that are being created, which have a fairly large data set, both as a starting data set and the generative data set,\u201d he said. \u201cWe\u2019ll see AI factories being fitted out with vast amounts of storage in the coming years.\u201d<\/p>\n

Here\u2019s the complete video interview, part of SiliconANGLE\u2019s and theCUBE\u2019s coverage of\u00a0theCUBE + NYSE Wired: AI Factories \u2013 Data Centers of the Future<\/a>\u00a0event:<\/p>\n<\/p>\n

Photo: SiliconANGLE<\/p>\n

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