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The commerce minister told a roomful of hyperscalers and power producers earlier this year that India is the world’s best location to build data centres, and put the opportunity at $200 billion. The supporting number, repeated everywhere since, is that installed capacity has grown from about 375 MW in 2020 to roughly 1,500 MW today.

That number is real. It is also the wrong number for the question everyone thinks it answers.

Almost all of that 1,500 MW is conventional cloud and colocation capacity, the kind that stores your bank statements and streams your video. AI infrastructure is a different physical object. It is denser, hotter, thirstier and vastly more expensive per square foot. Average rack densities in Indian facilities have already climbed from about 8 kW to 17 kW in two years and are heading towards 30 kW by 2027. Measured on that basis, India’s position is considerably weaker than the headline suggests, and the gap with the countries it is benchmarking itself against is not closing.

India’s largest operational GPU cluster is Yotta’s 20,736-accelerator deployment in Greater Noida, roughly 30 to 40 MW of IT load in a 60 MW facility. The national fleet is somewhere between 100,000 and 200,000 accelerators, spread thinly across many sites. The number of live Indian sites drawing 100 MW or more of GPU load is zero.

The comparison is uncomfortable.

Approximate figures, 2026

Total data centre capacity

Mixed-use cloud regions

Live sites ≥100 MW GPU load

Pipeline ≥100 MW by 2028

Several ≥1 GW campuses

xAI’s Colossus campus in Memphis runs roughly 770,000 GPUs at about a gigawatt, built in phases of three to four months on behind-the-meter gas turbines. OpenAI’s Abilene site was at about 0.3 GW in April, on its way to 1.2 GW, inside a seven-site programme totalling more than 9 GW. China reached 32 GW of installed capacity at the end of 2025 and has made ten-thousand-card clusters an explicit industrial-policy target, with hundred-thousand-card systems now in deployment and state approval for the purchase of over 400,000 H200s this year alone.

India’s entire national GPU fleet is smaller than what sits inside one American campus. India’s largest planned 2026 site, at 120 MW, is about a tenth of Colossus as it stands today. The Americans build a Jamnagar-sized cluster in a quarter. India plans to build one in two years.

The regional race is closer than anyone admits

Set the superpowers aside, and India’s actual peer group is Southeast Asia. Here the picture is more even, and more urgent.

Thailand has no live 100 MW GPU site either. But it has between five and eight projects in the 100 to 400 MW range under construction or approved, including a ByteDance AI hub of around 400 MW, a 300 MW facility at Rayong, and four Board of Investment approvals totalling 376 MW of IT load. Most land in 2027 and 2028. Vietnam’s flagship AI facility, Viettel’s Hoa Lac 2 in Hanoi, is 30 MW, the same order as India’s best, and it has a 200 MW campus designed for about 100,000 GPUs in the pipeline.

If everything India has announced arrives on schedule, the country ends 2028 with roughly 0.6 to 1.0 GW of AI-dedicated capacity, or 500,000 to 800,000 H100-equivalents. That is a tenfold improvement and it puts India clearly ahead of both. If delivery slips by eighteen months, which is the historical norm for Indian infrastructure of this size, the lead disappears.

The constraint is not what the argument is about

Indian debate has fixed on power and water. On power, the national arithmetic is reassuring and beside the point. At 5 to 7 GW, the sector would draw 40 to 50 TWh a year against 520 GW of installed generation and a peak demand of 240 to 250 GW. That is not a national problem.

The local problem is severe. A hyperscale project needs 100 to 500 MW at one location, running flat out around the clock, and urban substations were not built for that. AI training loads are also unusually hard on grids, stepping tens of megawatts in milliseconds in ways that even American utilities are........

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