India’s AI rush thrusts Nvidia to centre stage

Sub: Sci

Sec: AWARENESS IN AI

Context:

Reliance Industries and Nvidia have announced a collaboration to build extensive AI infrastructure in India, supporting the booming demand for AI services and providing essential computing power to startups.
Nvidia’s GPUs, which lead the AI hardware market due to their unmatched performance, have made the company a major player with a market cap exceeding $3 trillion, second only to Apple.
Key Project:
- Nvidia will support Reliance in establishing a 1GW data centre in Jamnagar, Gujarat. This facility will bolster AI capabilities within India, complemented by additional deployments of Nvidia GPUs across other Indian enterprises.

Other Major Deployments:

Tata Communications is deploying Nvidia Hopper GPUs for public cloud infrastructure and plans to upgrade to Nvidia’s next-gen Blackwell GPUs in 2025.
Yotta Data Services provides customers with Nvidia’s NIM and NIM Agent Blueprints for AI applications, attracting clients like Sarvam AI, Innoplexus, and Zoho.
E2E Networks operates across India, the Middle East, and Asia-Pacific, offering high-performance Nvidia GPU-powered cloud services.
Netweb is expanding its Tyrone AI systems based on Nvidia MGX architecture to optimize enterprise data centre workloads.

Government Support and Domestic Computing Initiatives:

The Indian government has launched a Rs 10,370 crore AI Mission to build local computing capacity and subsidize access for startups and researchers.
Plans include a public-private partnership for developing AI infrastructure, with Rs 4,564 crore dedicated to creating computing infrastructure and a provision for scalability based on demand.

About graphics processing unit (GPU):

A GPU is an electronic circuit designed for high-speed mathematical computations, crucial for applications like graphics rendering, machine learning (ML), and video editing.
Unlike central processing units (CPUs) that handle multiple general tasks, GPUs excel in performing the same operation on numerous data values simultaneously, making them ideal for compute-intensive tasks.

Why GPUs Are Important?

Originally, GPUs were developed solely for controlling image displays. However, advancements like Nvidia’s CUDA software in 2007 enabled general-purpose parallel processing on GPUs.
This made GPUs highly efficient for various tasks, especially those requiring substantial computing power, such as AI, ML, and simulations.
Today, GPUs power a broad range of applications beyond graphics, including finance, defense, and research.

Key Evolution Milestones:

Early Graphics Controllers: Initially, non-programmable graphics controllers managed displays, heavily relying on CPUs.
First GPUs: The first GPU aimed at consumer markets emerged in the late 1990s for gaming and CAD. In 1999, Nvidia released the GeForce 256 GPU.
CUDA Introduction: In 2007, Nvidia’s CUDA expanded GPU programming access, enabling use in AI, ML, and other compute-heavy applications.

Practical Applications:

Gaming: GPUs are critical for complex game rendering.
Professional Visualization: Used in CAD, medical imaging, and video editing.
Machine Learning: GPUs accelerate ML model training and can be accessed via cloud-based options for quicker results.
Blockchain: Many blockchains, especially those using proof-of-work models, rely on GPUs for transaction processing.
Simulation: Applied in areas like weather forecasting and vehicle design for high-fidelity simulations.

GPU Variants

Discrete GPUs: Standalone chips dedicated to intensive tasks.
Integrated GPUs (iGPUs): Combined with CPUs on a single chip, commonly found in laptops and smartphones.
Virtual GPUs: Software-based GPUs that enable cloud-based processing without physical hardware maintenance.

GPU vs. CPU:

While CPUs manage general system control and multitasking, GPUs specialize in repetitive, parallel tasks like ML. This distinction makes GPUs better suited for tasks needing vast computational power, such as real-time simulations and AI training models.

GPU vs. Graphics Card:

A graphics card is the hardware that houses the GPU along with components like VRAM and cooling systems. The GPU is the core of this card, performing the computational tasks, whereas the card itself connects to the motherboard to manage display functions.