Ola Chief Bhavish Aggarwal recently met with Arm CEO Rene Haas in Taiwan after migrating Ola's workload from Microsoft Azure and AWS to its own homegrown cloud platform, Krutrim.
“Wonderful meeting Rene Haas, Arm CEO in Taiwan. Lots of exciting things in store to enable AI compute for India!” Aggarwal posted on X.
Krutrim, India’s first AI unicorn, recently launched Krutrim AI Cloud, its own cloud platform for enterprises, researchers, and developers. Recently, Aggarwal said the world's largest talent density for semiconductors outside the US' Bay Area is in Bengaluru.
"But nobody is working on an Indian chip. The problem is not the talent. My peer set of entrepreneurs haven't given them a platform," Aggarwal said. “We've to make that platform...to build a full stack compute in India," he added.
Krutrim Cloud provides access to state-of-the-art AI computing infrastructure, Krutrim’s foundational models, and other open-source models such as Meta’s Llama 3 and Mistral. This platform will enable developers to run and build LLMs at a fraction of the costs currently offered by other cloud service providers.
With this development, Ola Krutrim will compete with Microsoft Azure, Google Cloud, and Amazon Web Services. These platforms have all experienced robust revenue growth in recent quarters,driven by increasing demand from businesses and enterprises of varying sizes.
The company has also announced Model-as-a-Service (MaaS) and GPU-as-a-Service offerings, allowing access to its foundational models and AI compute infrastructure.
British chip designer Arm Holdings anticipates that 100 billion Arm devices worldwide will be AI-ready by the end of next year, according to Haas, who made the announcement on Monday at the Computex forum in Taipei.
Moreover, the company aims to gain more than 50% of the Windows PC market in five years as Microsoft and its hardware partners prepare to launch a new batch of computers based on the British firm's technology.
In India, HCLTech recently partnered with Arm To build custom silicon chips for AI workloads.
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