CHINA ELIMINATES 12,000 ‘OBSOLETE’ UNIVERSITY DEGREES IN PUSH TO PREPARE FOR THE AI ERA
CHINESE UNIVERSITIES SCRAP 12,000 DEGREE PROGRAMS AS AI RESHAPES JOB MARKET DEMANDS
@brahma_4u You cannot ask the consumer to change. A company/product/brand needs to change this. if the babu system is resolved our companies will get more opportunities than not.
This is because India does not make these chips. Unfortunately everything being dollarized becomes significantly expensive. You start from basic materials to highly end datacenters everything is priced in dollars. You cannot start from the middle and make it affordable.
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.