Google Steps Up India AI Game with Trillium TPUs and Key Partnerships Google Cloud and Google DeepMind have teamed up with IIT Madras to support the launch of Indic Arena, operated by the AI4Bharat centre

Google is expanding its local hardware capacity for its customers in India. The boost to local compute comes through the company's AI Hypercomputer architecture with the latest Trillium TPUs, which Google says will help enable businesses and public sector firms to train and serve their most advanced Gemini models in the country.

What are Trillium TPUs?

Announced last year, Trillium is Google's sixth-generation TPU, and is dubbed as the most performant and most energy-efficient TPU to date. These Google TPUs are considered to run machine learning (ML) models faster and cheaper than regular CPUs or GPUs.

Google said that Trillium TPUs recorded a 4.7x increase in peak compute performance per chip as compared to the previous TPU V5e. The latest - gen TPU featured High Bandwidth Memory (HBM) capacity and bandwidth and doubled Interchip Interconnect (ICI) bandwidth.

"Additionally, Trillium is equipped with third-generation SparseCore, a specialized accelerator for processing ultra-large embeddings common in advanced ranking and recommendation workloads. Trillium TPUs make it possible to train the next wave of foundation models faster and serve those models with reduced latency and lower cost. Critically, our sixth-generation TPUs are also our most sustainable: Trillium TPUs are over 67% more energy-efficient than TPU v5e," the company said in a blog post last year.

Potential Gamechanger for India's AI landscape

India is scrambling to compete with the West and China in the AI space. Of late, the country has taken a myriad of initiatives to accelerate this growth. From a pure infrastructure point of view, India has earmarked INR 10,371.92 crore to scale up the local AI infrastructure, and deploy over 10,000 GPUs through public-private partnerships.

Google's deployment of the latest AI TPUs could also have a big impact on the overall efforts to achieve AI parity.

Experts believe that Trillium TPUs unlock a level playing field in access to advanced compute resources previously available only to large global players. It is set to enable them to build, train, and deploy complex AI models more efficiently and cost-effectively. This infrastructure is said to boost reduces time-to-market, fosters experimentation, and enhances product scalability.

Moreover, it encourages homegrown innovation in deep learning, generative AI, and edge AI applications. Combined with India's growing developer base and strong data ecosystem, this step could significantly amplify the capabilities of early-stage AI ventures and fuel the next wave of indigenous AI breakthroughs, according to Yashraj Bhardwaj, Co Founder and COO at Petonic AI.

With IndiaAI's national compute cluster now going past 38,000 AI units, local access to such cutting-edge infrastructure is estimated to further increase efficiency. This local accessibility to cutting - edge infrastructure can cut AI compute costs by as much as 40% and reduce model training latency by nearly 60%, according to Rishi Verma, Head of AI Financial Software and Systems (FSS). This effectively sets the stage for large-scale, production-ready AI innovation to be conducted entirely within India.

Aniket Tapre, Founder & CEO of Neural Arc, further breaks it down, noting that there's a big compute barrier. Earlier, many Indian AI teams had to rely on external cloud regions or overseas data centers, suffering from latency, cost drag and regulatory friction. With Trillium TPUs available locally, those constraints fall away.

Secondly, the local availability of such advanced hardware shifts India from being a consumption economy of AI solutions to being a creator economy of models, systems, and products, Tapre added.

"As a founder I recognise that this is the moment when we can build indigenous models tailored to Indian languages, cultural nuances, regulatory contexts and business realities not merely adapt global models. Thirdly, this move is about sovereignty of innovation. By hosting and training AI inside India, Indian enterprises and startups retain control of their data, modeling, deployment and business logic. That is strategic for trust, for regulation and for geopolitics of AI," Tapre said.

Shashank K Menon, founder and CEO of Hyderabad-based Vasco Tech, expressed similar sentiments, saying that it could be extremely beneficial for both early - stage startups as well as established players. Other benefits include reduced R&D efforts in AI.

"We have access to multiple LLMs, but the backbone, which I believe is infrastructure for compute has been missing. Google bringing Trillium TPUs to India is huge, and enables us to train and deploy models at scale without depending on foreign cloud compute which gets very expensive, very fast," Menon said.

Experts believe this will help lower the barrier for R&D in the AI sector significantly, as well as keep all the data in India. This is something Google echoes too.

"By unblocking new opportunities for high-performance, low-latency AI applications we can help customers meet India's data residency and sovereignty requirements," it said in the post.

Google's India AI focus, sovereignty and more

Apart from the latest TPUs, Google is doubling down on its push for the local AI ecosystem. The company said that it is committing to launching the most powerful Gemini models in India with full data residency support.

It is also rolling out Gemini 2.5 Flash as well as introducing a preview of Document AI for helping Indian businesses automate document processing.

Moreover, the company also announced that Google Cloud and Google DeepMind are teaming up with IIT Madras to support the launch of Indic Arena, which is operated by the AI4Bharat center at IIT Madras.

"This platform will allow users from all over India to anonymously evaluate and rank AI models on tasks unique to India's rich multilingual landscape. To support this initiative, we are providing cloud credits to power this critical, community-driven resource," Google said.

Sovereignty, as mentioned above, has also remained a big challenge for the local AI aspirants.

"In the BFSI sector, where data residency, privacy, and compliance are absolutely essential, localized computing is a real game-changer. It allows banks and payment processors to train, fine-tune, and deploy models while staying within the country's regulatory framework…," Verma added.

Experts believe these kinds of investments open the door for startups and established companies alike, nurturing homegrown innovation that truly reflects India's diverse linguistic, cultural, and sectoral landscape.

"⁠For founders, this ecosystem is transformational. It gives them the platform, legitimacy, and unique India-specific datasets needed to build AI products with deep defensibility solutions that start in Bharat, solve at scale, and ultimately compete on the world stage," Sameer Mathur MD and Founder of Roinet Solution told Entrepreneur India.