> Open source models would still need to be trained on Nvidia’s HW? yes, but how much hardware would really be required for training in this scenario and does

> Open source models would still need to be trained on Nvidia’s HW? yes, but how much hardware would really be required for training in this scenario and does it support a stratospheric valuation? Training models versus using models are dramatically different processes. Training is very intensive in cycles and memory but it is one-off and centralized to the model developer. Inferencing is in contrast relatively less intensive but its use is continuous and decentralized to potentially billions of users. Consider also that we are likely in the brute-force era and model size will keep shrinking, potentially quite dramatically. While models have a lifecycle and will, in general, need to be retrained with new data, be specialized to private data etc, once a model is released it may be used for years without change. In fact you want this stability and longevity to release things in production. My guess (but it is only a guess in what is a very uncertain landscape) is that just like mass-produced general purpose CPU's eventually won the economics game and became the dominant chip design (disrupting both the RISC workstations and HPC supercomputer clusters of the time), inferencing chips will win the mass produced "AI" economics game and will become the dominant accelerator, co-processor or super-sized CPU design. The important commonality between inferencing and training is that both are numerical linear algebra. The economic model that will successfully commercialize inferencing is thus likely to drive the entire architecture for this specialized type of compute - the mass produced inferencing architectures will subsidize developing the higher-end training configurations. Of-course, even if that reading is accurate, Nvidia could still somehow compete, but it could also be disrupted fairly structurally.