6. GPU Technology: The Engine of the Modern Digital Economy

Originally developed in 1999 to enhance video game visuals, the Graphics Processing Unit (GPU) has evolved into the foundational infrastructure for the global artificial intelligence (AI) revolution. As of February 2026, GPUs are no longer just \'gaming chips\' but are strategic national assets central to sovereign AI and highperformance computing. Core Summary of GPU Evolution and Utility • Parallel Architecture: Unlike the Central Processing Unit (CPU), which handles complex tasks sequentially (one after another), a GPU contains thousands of smaller, specialized cores designed to handle many simple calculations simultaneously.• From Pixels to Parameters: GPUs excel at \'number-crunching\' for millions of pixels on a screen. This same mathematical capability is now used to process billions of \'parameters\' in neural networks, making GPUs indispensable for training Large Language Models (LLMs). • The Rendering Pipeline: The GPU operates through a four-stage process—Vertex Processing (positioning), Rasterisation (converting geometry to pixels), Shading (adding color/light), and Writing to Frame Buffer (final image display). • Memory Bandwidth: High-end GPUs utilize specialized Video RAM (VRAM) and high-speed connections to prevent data bottlenecks, ensuring that the massive volume of data required for AI and 3D graphics can be moved at near-instantaneous speeds. • Market Dominance: As of early 2026, Nvidia maintains a near-monopoly with approximately 90% market share in data-center GPUs, largely due to its proprietary CUDA software ecosystem, which makes it difficult for developers to switch to competitors like AMD or Intel. • Sustainability Challenges: Training a single large neural network can consume energy equivalent to running a home air conditioner for several hours daily, raising critical questions about the environmental footprint of the AI industry. Key Definitions • CUDA (Compute Unified Device Architecture): Nvidia\'s proprietary software platform that allows developers to use GPUs for general-purpose mathematical processing beyond graphics. • Tensor Cores: Specialized hardware within modern GPUs designed specifically to accelerate the matrix multiplication required for deep learning. • Die: The actual flat piece of silicon on which the GPU\'s circuits are etched. • Hukou System Analog (The Parallelism Metaphor): If a CPU is like a single expert teacher grading one paper at a time, a GPU is like 1,000 students each grading one page of the school\'s exams simultaneously. Constitutional & Legal Provisions • Competition Act, 2002 (India): Relevant in the context of global investigations into GPU \'bundling.\' Section 3 (Anti-competitive agreements) and Section 4 (Abuse of dominant position) empower the Competition Commission of India (CCI) to investigate if a firm uses its market power to exclude rivals or lock-in customers. • IT Act, 2000: Governs the legal framework for the digital economy in India, though it is currently being replaced by the Digital India Act (2026) to address emerging technologies like AI and semiconductor security. • EU Antitrust Laws (Articles 101 & 102 TFEU): Currently being invoked by European regulators to investigate whether dominant GPU makers are using \'product tying\' (forcing software with hardware) to stifle competition. India’s Semiconductor Strategy (Semicon 2.0)• IndiaAI Mission: In February 2026, the government announced an expansion of the national compute pool, aiming to add 20,000 more GPUs to the existing 38,000 to support domestic startups. • Indigenous Design: Under Semiconductor 2.0, India is shifting focus from just \'assembly\' to \'designing\' full-stack Indian IP, including RISC-V based processors to reduce reliance on global proprietary architectures. Conclusion The transition of GPUs from \'discretionary\' components to \'core infrastructure\' marks the beginning of the Fifth Industrial Revolution. While the technological leaps are unprecedented, the concentration of market power and the escalating energy demands present significant regulatory and environmental hurdles. For India, achieving \'Sovereign AI\' requires not just the procurement of thousands of GPUs but the development of indigenous chip-design capabilities to ensure long-term strategic autonomy. UPSC Relevance • GS Paper III: Science and Technology- developments and their applications and effects in everyday life; Awareness in the fields of IT, Computers, Robotics, and AI. • GS Paper III: Infrastructure: Energy (Data center energy consumption); Economy (Monopolies and market dominance). • GS Paper II: Bilateral, regional and global groupings and agreements (India-U.S. Trade Framework 2026 regarding tech products). • Prelims Focus: Difference between CPU, GPU, and TPU; CUDA ecosystem; India’s Semiconductor Mission (ISM) targets; and global antitrust regulatory bodies.