> You can train, or at least run, llms on intel and less powerful chips
The claimed training breakthrough is an optimization targeting NVidia chip, not something that reduces NVidia's relative advantage. Even if it is easily generalizable to other vendors hardware, it doesn't reduce NVidia's advantage over other vendors, it just proportionately scales down the training requirements for a model of a given capacity. Which, maybe, very short term reduces demands from the big existing incumbents, but it also increases the number of players for which investing in GPUs for model training at all is worthwhile, increasing aggregate demand.
It's not an optimization targeting Nvidia chips. It's an optimization of the technique through and through regardless of chip
But your point is well taken and perhaps both mine and GP's metaphors break down.
Either way, we saw massive spikes in demand for Nvidia when crypto mining became huge followed by a massive drop when we hit the crypto winter. We saw another massive spike when LLMs blew up and this may just be the analogous drop in demand for LLMs
You both seem to be talking past each other. There were a number of optimizations that made this possible. Some were with the model itself and are transferable, others are with the training pipeline and specific to the Nvidia hardware they trained on.
> What is stopping huawei or other Chinese vendors to make chips on deepseek specification
What is "deepseek specification"? Deepseek was trained on NVDA chips. If chinese vendors could build chips as good as NVDA it wouldn't have such a dominant position already, that hasn't changed
