India too wants to make its place in this age of generative AI. However, there are two formidable challenges — there is lack of hardware accelerators suited for AI requirements and a shortage of talent.

LLM training is very capital intensive. Here shortage of talent is a big issue. There are only a few people in the world who really know how to train LLMs. There are issues of curating data and carefully running evaluation metrics. It must be ensured that the models are generalizable. Very few people in the world know how to do all this. Most of them are US-based and are working in a handful of companies — OpenAI, Facebook, Anthropic, DeepMind and Mistral. The knowledge of training a model that has GPT-4 capability is concentrated both individual-wise and geography-wise.

Computing capacity (compute) is another challenge in building a large AI system. Then there are issues of algorithmic innovations and datasets.

AI accelerators are specialized automatic data processing systems. They accelerate computer science applications, especially artificial neural networks, machine visualization and ML.

India has to set up hardware accelerators, and then train them. This is a difficult task. India alternatively can think in terms of ‘inference hardware’. Inference is the process of running live data through a trained AI model to process the data. AI hardware is coupled with software. Nvidia’s GPUs are coupled with the CUDA libraries, needed to make the good use of hardware. It is a big advantage for Nvidia.

India can use open-source models — Llama from Facebook. India can take these base models and try to build on top of them. It can bootstrap off them.

This is the summarization of thinking of Arvind Srinivas, CEO of Perplexity AI who is making waves in Silicon Valley.