India is after developing indigenous LLMs. The critical issue is whether India has the computing infrastructure to support the AI workloads. Even training the LLMs require massive processing capabilities. Both the US and China have an edge here — they have an advanced supercomputing infrastructure.
India generates huge volume of data but it is unstructured and requires lot of cleaning before being used for training. There is a need for better industry-academic collaboration. There should be sustained investment.
India requires AI infrastructure including data centers with high performance computing (HPC), specialised GPU clusters and efficient cooling technologies. This will support large scale AI training and inferencing.
Data centers and cloud infrastructure should support storage and high performance computing.
As AI adoption expands, there will be greater need for AI-ready infrastructure. There should be service providers to take on the risks and invest in high-performance GPUs. These services should be available on a cloud model. It ensures scalability and operational efficiency.
Indian data centers are not optimised for AI workloads which require specialised hardware such as Nvidia A100 GPUs or TPUs.
AI hardware should be both compute-intensive and energy-intensive.
AI optimized hardware includes GPUs, FPGAs (field programable gate array) and other accelerators to speed up model training and inference. These data centers must be power efficient, should have cooling facilities and bandwidth. They should have robust framework to protect sensitive data and proprietary AI models. They should take cyber security measures.
India should provide education to prepare talent pool of people. Future ready data centers must integrate AI-specific accelerators. There should be adoption of renewable energy sources.
India has the potential to emerge as global AI powerhouse if there is investment in the right infrastructure, policy support and talent development.
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