Harnessing the power of GPUs: India’s path to AI leadership

By Vinay Chhabra, Co-Founder & Managing Director, AceCloud, a brand of Real Time Data Services

Artificial Intelligence (AI) is a transformative force that is reshaping industries worldwide. India’s AI market is projected to reach $17 billion by 2027, according to the NASSCOM and BCG report on AI Powered Tech Services: A Roadmap for Future Ready Firms. However, to fully harness the power of AI and Machine Learning (ML), businesses must leverage Graphics Processing Units (GPUs). GPUs are essential for optimising parallel tasks and handling the large datasets required by AI and ML algorithms, empowering companies of all sizes to implement data-driven operations.

Evolution of GPU in India

GPUs were initially introduced to enhance video game graphics. However, today, they have evolved into specialised electronic circuits designed to handle complex calculations and data processing tasks. They are essential tools for industries requiring high-performance computing, from healthcare to finance. According to Credence Research Report, India’s cloud GPU market is projected to surge from USD 80.21 million in 2023 to USD 1,319.54 million by 2032 with a CAGR of 36.50% highlighting the growing demand for GPUs across data-driven industries.

Capitalising on the growing demand, India can strategically position itself as a key player in the global GPU landscape by focusing on AI development and software optimisation. By creating efficient AI systems that minimise reliance on costly hardware, India can significantly contribute to global AI advancements. This synergy between GPU technology and India’s software expertise has the potential to enable digital transformation for businesses of all sizes.

Understanding GPU functionality and its role in AI and ML algorithms

Unlike CPUs, which are designed for sequential task processing, GPUs have a parallel architecture with thousands of cores that can execute many operations simultaneously. This parallelism enables them to process vast amounts of data at high speed, which is critical in AI and ML tasks involving complex algorithms and massive data inputs. For example, a deep learning model trained on a GPU can analyse and process data much faster than a CPU, as the GPU can perform multiple matrix operations concurrently.

The need for GPUs in data handling is growing alongside data volumes, projected by IDC to reach 175 zettabytes by 2025. With AI models becoming increasingly complex and data-intensive, the ability of GPUs to manage and accelerate parallel tasks makes them indispensable for both training and deploying AI applications. This unique architecture enables GPUs to handle AI workloads more efficiently, meeting the rising demand for rapid data processing and analysis in real time.

Industry applications of GPUs

1. Machine Learning frameworks: GPUs support ML frameworks, like TensorFlow and PyTorch, which are crucial for training complex models quickly. For example, in autonomous driving, GPUs process extensive sensor data, speeding up model training and enhancing accuracy. Additionally, GPUs reduce the time needed to iterate and optimise models, leading to faster advancements in AI applications.
2. Data intensive applications: With high Video Random Access Memory (VRAM) and fast memory bandwidth, GPUs excel in managing data-intensive applications. In healthcare, GPUs assist in analysing large datasets, such as genomic data, for faster insights and better decision-making. Their ability to process vast amounts of data in parallel also supports real-time diagnostics and treatment recommendations.
   
3. Realistic visual effects: In the gaming and animation sectors, GPUs handle intensive 3D rendering, allowing for high-quality visuals and lifelike graphics. This capability supports industries like motion pictures industry, where realistic visual effects are essential. GPUs’ rendering efficiency also reduces production time, making them indispensable for content creation under short deadlines.
   
4. High computational power: GPU-accelerated virtual desktops give remote professionals access to high computational power, benefiting fields like graphic design, engineering, and architecture where intensive applications are needed. This also enables collaborative work on resource-heavy tasks, as multiple users can simultaneously access high-powered virtual workstations.

Road ahead

The future of GPU networks is brimming with potential, particularly in the fields of AI and ML. In India, the rapid expansion of digital payments, the telecom data revolution, and government initiatives promoting digital technologies have created an urgent need for more robust digital infrastructure, with a particular focus on GPUs.

As AI and ML technologies continue to advance, GPUs will play a pivotal role in driving innovation, enabling the development of increasingly complex models and accelerating data processing. This will be especially critical in areas like natural language processing, robotics, and autonomous driving, where real-time performance is essential.

As nations worldwide vie for dominance in the GPU space, India’s role will be vital. By leveraging its software expertise to create more efficient AI systems that reduce reliance on hardware, India has the potential to reshape the future of tech leadership in the AI era.