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Nvidia powers AI development in Japan and Indonesia
Nvidia is deepening its presence in Japan and Indonesia through partnerships with local cloud providers and tech companies to build sovereign AI infrastructure and local large language models
Nvidia is expanding its footprint in the key Asian markets of Japan and Indonesia, partnering with local cloud providers and technology companies to build artificial intelligence (AI) infrastructures and foster sovereign AI development.
In Japan, Nvidia is working with SoftBank, GMO Internet Group, Highreso and KDDI, among others, to deploy AI supercomputers and cloud services nationwide. The initiatives, backed by Japan’s ministry of economy, trade and industry, aim to bolster critical compute resources across various sectors.
SoftBank, for example, is leveraging Nvidia’s technology to build Japan’s most powerful AI supercomputers, while GMO Internet Group is launching the country’s first local cloud offering with full-stack Nvidia H200 Tensor Core graphics processing units (GPUs).
Cloud provider Highreso is establishing dedicated AI datacentres while telecoms giant KDDI is launching AI computing infrastructure to support generative AI (GenAI) and specialised large language model (LLM) development.
“Japan’s companies stand to benefit tremendously from the new industrial revolution powered by AI,” said Jensen Huang, founder and CEO of Nvidia. “Company employees will supercharge their speed and productivity by automating work with AI agents.
“Industrial companies of tomorrow will operate dual factories – new AI factories to produce software intelligence for the products and machines their factories make today,” he said.
“Working with Nvidia, Japan’s cloud providers are building the AI factories essential to reinventing the country’s automotive, robotics, telecommunications and healthcare industries for the age of AI.”
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In Indonesia, Southeast Asia’s largest economy, telecoms giant Indosat has used the Nvidia NeMo development platform and Nvidia inference microservices to build a family of open-source Indonesian LLMs. The models will enable developers to build GenAI applications in Bahasa Indonesia and other local languages.
“Sahabat-AI is not just a technological achievement, it embodies Indonesia’s vision for a future where digital sovereignty and inclusivity go hand in hand,” said Indosat CEO Vikram Sinha. “By creating an AI model that speaks our language and reflects our culture, we’re empowering every Indonesian to harness advanced technology’s potential.”
Tailored applications
Indosat is also partnering with Accenture to develop AI applications based on Sahabat-AI and the Nvidia AI Enterprise software platform. Leveraging Accenture’s AI Refinery platform, the applications can be tailored for organisations in financial services, energy and other sectors while ensuring data sovereignty.
US healthcare AI startup Hippocratic AI is among the first to tap Sahabat-AI and Indosat’s sovereign AI cloud, which was launched earlier this year, to build digital agents powered by its trillion-parameter architecture that leverages specialised healthcare LLMs.
These digital agents are expected to be able to engage in human-like conversations and build rapport and trust with patients across Indonesia, while also allowing human nurses and medical professionals to focus on critical duties by offloading time-consuming tasks.
In the public sector, Indosat and India’s Tech Mahindra are helping the Indonesian government build a citizen services platform. This is part of a sovereign AI initiative that will incorporate an AI chatbot powered by Sahabat-AI to answer queries in Bahasa Indonesia for citizen and resident services.
Nvidia’s expanding footprint in Japan and Indonesia follows recent efforts to drive adoption of its compute platform and foster collaborations with key technology players across the Asia-Pacific region.
In India, it’s supporting companies in building LLMs tailored for the country’s regional and linguistic requirements. It has also developed a small language model tailored for Hindi language tasks by pruning a larger model while preserving accuracy and incorporating Hindi training data.
