G42 deal boosts India AI ambitions

Abu Dhabi technology group G42 and the Government of India have formalised terms to deploy Condor Galaxy India, an 8-exaflop artificial intelligence supercomputing cluster designed to strengthen the country’s sovereign AI infrastructure and widen access to advanced computing for research, public services and industry.

The agreement, witnessed by UAE President Sheikh Mohamed bin Zayed Al Nahyan and Prime Minister Narendra Modi during Modi’s official state visit to Abu Dhabi, advances the UAE-India digital infrastructure memorandum of understanding signed in 2024. The exchange involved Mansoor Al Mansoori, chief executive of G42 International, and Vikram Misri, Foreign Secretary of India, placing the project within a wider strategic partnership covering technology, energy, space, defence and advanced industry.

Condor Galaxy India will comprise 64 Cerebras CS-3 systems, placing it among the most powerful AI compute systems planned for deployment in the country. The cluster is expected to operate as a national-scale asset for training and running large AI models, with applications spanning health and genomics, energy, geospatial analytics, education, climate modelling and public-sector digital services.

G42 will work with the Centre for Development of Advanced Computing, better known as C-DAC, on installation, deployment, operation and maintenance. The arrangement is structured around India-defined governance frameworks, with data expected to remain within national jurisdiction. That feature is central to the project’s political and commercial significance, as governments increasingly treat AI compute, data security and model development as strategic infrastructure rather than conventional technology procurement.

Mansoor Al Mansoori described India as “one of the world’s great innovation economies” and said the deployment showed how energy and compute could be converted into “sovereign governed nation-scale intelligence”. The framing reflects a broader shift in AI policy, where countries are seeking local computing capacity to reduce dependence on overseas cloud facilities and ensure sensitive datasets are processed under domestic rules.

The project also gives the UAE a larger role in Asia’s AI infrastructure build-out. G42, backed by Abu Dhabi capital and positioned as a global AI and cloud computing group, has been expanding its footprint through partnerships in data centres, language models, health technology and sovereign cloud systems. Its collaboration with Cerebras Systems has already produced Condor Galaxy installations in the United States, and the India deployment extends that network into one of the world’s fastest-growing digital economies.

Cerebras’ role is equally significant. The CS-3 is built around the company’s third-generation wafer-scale engine, a processor architecture designed for large AI workloads. Each CS-3 system is powered by a chip with about 4 trillion transistors and 900,000 AI-optimised cores, offering a different route to scale from the graphics processing unit clusters that dominate much of the AI market. Cerebras completed a major Nasdaq listing in May 2026 under the ticker CBRS, raising about $5.55 billion and drawing strong investor attention to specialised AI infrastructure.

For India, Condor Galaxy India comes as policymakers are trying to expand domestic compute under the IndiaAI Mission, a ₹10,372 crore programme approved in 2024 to build AI computing capacity, support start-ups, create datasets, finance innovation and develop locally relevant models. The mission initially centred on making 10,000 or more GPUs available through public-private partnerships, with later initiatives aimed at broadening affordable compute access for researchers, start-ups, students, government bodies and smaller enterprises.

The supercomputing cluster is expected to complement that approach by adding exaflop-scale capacity for frontier workloads. Its backers say it will serve both institutions and emerging innovators, potentially reducing barriers for groups that lack access to high-end infrastructure. For universities and research laboratories, such capacity could accelerate work in life sciences, materials research, agriculture and weather modelling. For start-ups, it could improve the ability to train or fine-tune models suited to local languages and sector-specific use cases.