> It takes substantial technical expertise to train very large models on GPUs. In the recently released GPT-4 Technical Report, OpenAI credits over thirty contr

> It takes substantial technical expertise to train very large models on GPUs. In the recently released GPT-4 Technical Report, OpenAI credits over thirty contributors just for compute infrastructure and scaling. This is called a silver lining for some (in case you were worried about gpt taking your job). Privacy requirements alone will in the near term force major companies to run their own inference (if not training). The expertise required are nearly identical to that of running large scale distributed computational graphs. This is an interesting diveragence from what happened with web. The backends started out simple before map-reduce and before deconstructing databases and processing distributed logs. With ML, we'll jump right into the complex backends in tandem with easy-picking early stage edge applications (which we see daily on HN).