 "As AI arms race heats up, India’s role will be crucial")
In the rapidly evolving landscape of artificial intelligence, a new battleground has emerged that’s reshaping the geopolitical tech scene: the race for GPU (Graphics Processing Unit) supremacy. Recent reports of nations stockpiling these powerful chips, essential for training large AI models, have thrust this previously niche hardware into the spotlight of international relations.
Trade Wars
The catalyst for this global GPU grab was the US government’s October 2023 export controls on advanced AI chips to China, which have now been tightened further in April 2024. These restrictions, aimed at curbing China’s AI capabilities, have had far-reaching consequences, sparking a worldwide rush to secure these critical components.
The numbers tell a compelling story. NVIDIA, the leading GPU manufacturer, saw its data centre revenue skyrocket to $18.4 billion in Q4 2023, a staggering 409 per cent increase year-over-year. This surge is largely attributed to the AI-driven demand for high-performance GPUs. Meanwhile, the global AI chip market, valued at $51.2 billion in 2023, is projected to reach $131.8 billion by 2028, growing at a compound annual growth rate of 20.8 per cent.
China’s response has been swift and decisive. Reports suggest that tech giants like Baidu and Tencent have been amassing NVIDIA’s A800 GPUs, the most advanced chips they can legally obtain. This stockpiling isn’t just about maintaining AI development pace; it’s a strategic move to buffer against potential future restrictions.
Strategic Assets
But the implications extend far beyond US-China relations. Countries worldwide are now recognising GPUs as strategic assets crucial for technological and economic competitiveness. The European Union, through its European Chips Act, has pledged €43 billion to double its global semiconductor market share to 20 per cent by 2030. Japan has earmarked $6.8 billion for domestic semiconductor development, while South Korea plans to invest $450 billion in its chip industry over the next decade.
This GPU arms race is more than a tech industry trend; it’s a microcosm of the broader geopolitical struggles in the AI era. The concentration of AI compute power is stark: a recent study by Stanford University’s Human-Centred AI Institute found that just 15 companies control 95 per cent of the world’s AI compute resources. This imbalance has profound implications for global AI development and deployment.
Impact Shorts
More ShortsThe situation raises critical questions about the future of global tech cooperation. Are we heading towards a world of tech isolation, where nations and regions develop parallel, incompatible AI ecosystems? The potential for divergence is real. China’s alternative GPU designs, like Huawei’s Ascend series, are gaining traction, with the company reporting a 130 per cent year-over-year increase in AI computing power sales in 2023.
Moreover, this race highlights the vulnerability of global supply chains. With NVIDIA and AMD dominating the high-end GPU market, controlling over 95 per cent of the discrete GPU market share, countries are acutely aware of their dependence on foreign suppliers for this critical technology. This realisation is driving not just stockpiling but also intensive efforts to develop domestic alternatives.
The environmental impact of this AI arms race cannot be ignored. Training a single large language model can produce over 626,000 pounds of carbon dioxide emissions, equivalent to the lifetime emissions of five cars. As nations ramp up their AI capabilities, the energy demands and environmental costs could be substantial.
As nations jockey for GPU advantage, we must consider the broader implications. The concentration of AI capabilities in the hands of a few countries could exacerbate global inequalities. There’s also the risk of an AI “cold war”, where technological barriers become new iron curtains. The World Economic Forum estimates that AI could add $15.7 trillion to the global economy by 2030, but the distribution of these gains is likely to be uneven.
Policymakers face a delicate balance act. They must weigh national security concerns against the benefits of open scientific exchange and global market efficiency. Too restrictive policies could stifle innovation and economic growth, while too lax an approach could compromise strategic interests.
The recent establishment of the US-EU Trade and Technology Council and the Quad’s focus on technology cooperation demonstrate attempts to navigate this complex landscape. As this high-stakes game unfolds, one thing is clear: the nation or alliance that secures GPU supremacy may well hold the keys to the AI kingdom, and with it, a significant edge in the geopolitical landscape of the 21st century.
India’s Emerging Role in the Global GPU and AI Landscape
In this high-stakes technological contest, India is positioning itself as a unique player, leveraging its software prowess and vast talent pool to carve out a niche in the AI ecosystem. While India may not currently be at the forefront of hardware manufacturing, its strengths in software development and IT services present significant opportunities in the AI and GPU space.
India’s software industry, valued at $227 billion in FY2022 and expected to reach $350 billion by 2026, forms the backbone of its technological capabilities. The country produces 1.5 million engineering graduates annually, creating a vast talent pool for AI and machine learning development. This human capital is India’s key advantage in the global AI race.
Recognising the strategic importance of AI, the Indian government launched the National Strategy for Artificial Intelligence in 2018. The strategy focuses on leveraging AI for economic growth and social inclusion, with a particular emphasis on healthcare, agriculture, education, smart cities, and infrastructure.
In the context of the GPU scramble, India should take a multi-pronged approach, one that is not just focused on hardware but leverages India’s strengths too, as we discuss below.
Software Optimisation
Indian tech companies and startups should focus on developing software solutions that optimise GPU usage, potentially reducing the hardware requirements for AI training and inference. This approach plays to India’s software strengths while addressing the global GPU shortage. Bengaluru-based startup Nference has developed an AI platform that optimises GPU usage for genomics and biomedical research. Their software allows for more efficient processing of large-scale biomedical data on existing GPU hardware. TCS (Tata Consultancy Services) has created a GPU-accelerated analytics platform called “ TCS BaNCS”, which optimises financial modelling and risk assessment tasks, reducing the need for extensive GPU resources. Researchers at IIT Bombay have developed “SparseDNN”, a compressed deep learning model that requires significantly less computational power while maintaining accuracy.
Cloud GPU Services
Indian cloud service providers like Tata Communications and Reliance Jio are expanding their GPU-as-a-service offerings, allowing businesses to access high-performance computing without the need for physical hardware ownership. Tata Communications offers “IZO™ Private Cloud” with GPU acceleration, allowing businesses to access high-performance computing resources on-demand. Reliance Jio, through its partnership with NVIDIA, provides cloud-based GPU services for AI development and deployment on its JioCloud platform.
Domestic Chip Development
While not yet at the scale of global leaders, India is making strides in chip design. The government’s $10 billion incentive scheme for semiconductor and display manufacturing is a step towards reducing dependency on foreign chips. Bengaluru-based Signalchip has designed India’s first indigenous semiconductor chips for 4G/LTE and 5G NR modems. The Centre for Development of Advanced Computing (C-DAC) has developed the “ PARAM Siddhi-AI” supercomputer, using a mix of imported and domestically developed components.
International Collaborations
India is leveraging (and should leverage further) its diplomatic ties to secure technology transfers and collaborations. The India-US Initiative on Critical and Emerging Technologies (iCET) includes provisions for high-performance computing and semiconductor supply chain diversification. Under the India-US iCET initiative, companies like Lam Research are setting up training programmes in India to develop semiconductor manufacturing skills. The Indian Institute of Science (IISc) has partnered with IBM to establish an AI research lab, focusing on natural language processing and computer vision applications.
AI Research Hubs
The establishment of AI research centres like ARTPARK (AI & Robotics Technology Park) in Bangalore aims to bridge the gap between academic research and industrial applications in AI and robotics. ARTPARK at IISc Bangalore is working on AI solutions for healthcare, including a project on AI-assisted robotic surgery. The Wadhwani Institute for Artificial Intelligence in Mumbai is developing AI solutions for social good, including an AI-powered tool for early detection of crop pests.
Talent Development
The NASSCOM FutureSkills initiative has partnered with NVIDIA to train over 500,000 IT professionals in AI technologies. The Indian government’s “ Responsible AI for Youth” programme aims to empower young students with AI skills, having trained over 11,000 students in its first phase.
AI for Social Good
Bengaluru-based Intello Labs uses computer vision and deep learning to assess the quality of agricultural produce, helping reduce food wastage. Niramai, an Indian healthtech startup, has developed a low-cost, AI-based breast cancer screening solution that’s particularly useful in rural areas.
Edge AI Development
Qualcomm, in collaboration with Indian partners, has set up an Innovation Lab in Hyderabad focusing on AI and IoT applications that can run efficiently on edge devices with limited GPU resources.
Open-Source AI Initiatives
The Indian AI community should actively contribute to global open-source projects. For instance, developers from various Indian tech companies have contributed to the development of Apache MXNet, an open-source deep learning framework. Similarly, Indian tech companies should join the OpenAI-led initiative to build software that facilitates switching between different AI chips.
India Needs to Rise to the Occasion
India’s approach to the GPU challenge showcases a different model in the global AI race—one that emphasises software innovation, talent development, and strategic international partnerships over hardware manufacturing dominance. This strategy could potentially offer a third path in the US-China tech rivalry, presenting India as a collaborative partner to both while maintaining its technological sovereignty.
However, challenges remain. India still lags in terms of AI infrastructure and faces a significant AI skills gap. A NASSCOM report indicates that India would need to train and re-skill over 4.5 million people in AI and related technologies by 2025 to meet industry demands.
As the global scramble for GPU dominance continues, India’s role will be crucial. Its software expertise could be key in developing more efficient AI systems that reduce reliance on hardware. Moreover, as countries seek to diversify their tech partnerships beyond the US-China axis, India’s growing AI capabilities and neutral stance could make it an attractive collaborator. While India may not be competing directly in GPU manufacturing, its software-centric approach to AI development could redefine the parameters of technological leadership in the AI era.
The author is a research scholar at Takshashila Institution, Bangalore. The views expressed in the above piece are personal and solely those of the author. They do not necessarily reflect Firstpost’s views.
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