Indian firms can get GPUs, which is not a problem (for now), but talent is a big, big problem. Indian companies just do not have it in them to pay people multi-million dollar compensation. An equally difficult problem is to convince such people to work in India. I feel that even if Silicon Valley-level comp was on offer, most people in that league would baulk at the idea of living in Indian cities. The solution is to open an office in Dubai or Singapore. That could also open the door to non Indian talent. A $10B spend is possible if 3-4 Indian ITES companies combine forces.
To train a GPT class 1T model from scratch - including failed runs, data acq clean rlhf, post-training, team/people will likely req $250M of compute on an aggressive 3-4mo schedule (i.e. more reserved GPUs), $500-600M all-in IF you do a dense one. MoE fp8 will cut costs by 1/10th depending on how many active params you have. If you want SOTA however, the budgets go significantly higher on test-time compute, post-training RL, and data/synthetic generations..and v. high on talent. Maybe $2-4B all-in. After that comes serving the model. The talent is key to get to SOTA/beat it - and then you have to ensure this is useful enough to have inference vol over time - for which the capital will come if there is usage / TAM. So this is not as much about raising $50-60B, or raising it all at once as the OP says - we are investors in mistral, sarvam, reflection and anthropic - and they all scaled capital over time as models got adoption, but the early bottleneck is more on talent GPUs at that scale where you can do interesting things.