How Sovereign Is India’s AI Compute, Really?
I've been looking at the shift from cloud-hosted AI to local, private and sovereign deployments, and one distinction keeps coming up: local AI and sovereign AI aren't necessarily the same thing.
A paper I found particularly useful for thinking about this is:
AI Compute Sovereignty: Infrastructure Control Across Territories, Cloud Providers, and Accelerators
Hawkins, Lehdonvirta & Wu — Oxford / Aalto
What I liked about the paper is that it doesn't treat sovereignty as binary. It breaks it into three layers:
- Where is the compute? — territorial control
- Who operates it? — cloud/provider ownership
- Who supplies the accelerators? — hardware/accelerator control
The authors' census of nine major public-cloud providers found 225 cloud regions across 43 countries, with 132 accelerator-enabled regions across 33 countries. Only 24 countries had training-relevant compute in the dataset.
India is an interesting example of why these layers matter.
In the paper's November 2024 snapshot, India had 5 accelerator-enabled regions, including 3 with training-relevant compute. The provider breakdown was 4 US-provider regions and 1 Chinese-provider region, which the authors describe as a form of “hedging” rather than dependence on a single foreign power.
But that snapshot is already dated.
By 2026, official Indian figures show 38,231 GPUs onboarded from 14 providers under the IndiaAI Compute framework, alongside 1,050 TPUs. India has also been setting up a 3,000-GPU secure national cluster for sovereign and strategic AI workloads, while another 20,000 GPUs were announced for addition beyond the existing capacity.
So, India's compute capacity has changed dramatically since the paper's dataset.
But the paper's deeper question is still relevant.
At the accelerator layer, it found that 95.5% of accelerator-enabled regions in its census were powered by US-owned accelerators.
That means: more compute in India doesn't automatically mean more sovereignty.
And I don't think the paper's argument is that India should try to build every component domestically either. More domestic compute can mean greater control and supply security, but data centres also bring significant demands on energy, water and land. The paper explicitly treats sovereignty as a trade-off rather than an objective that is automatically good.
That seems to be where the industry is heading as well.
NVIDIA and HPE are pushing sovereign AI heavily from the infrastructure/compute side, while Microsoft and IBM are building increasingly explicit AI control-plane capabilities around deployment, governance and operations. Lyzr is another interesting example at that layer, taking a more framework-agnostic approach to governing agents across different stacks and environments.
Which brings me to the part I find most interesting:
Maybe sovereign AI isn't ultimately about owning every component. Maybe it's about controlling the layers that actually matter for a particular threat model — compute, data, models, identity, deployment, governance, or the control plane itself.
For a local-LLM user, that might simply mean local models + local inference + local data. For a government or enterprise, the definition could be much broader.
So for India: what should “sovereign AI” actually mean?
Is owning the GPUs enough?
Is domestic cloud infrastructure enough?
Do we need domestic models and chips?
Or is strategic autonomy through managed interdependence the more realistic goal?