And even though NVIDIA is better place as they do produce something, but the something in play has little value out of the AI bubble.
NVIDIA could be left holding the bag on a super increased capacity to produce something that nobody wants anymore (or at least nowhere near at the levels we have now) so they are still very much exposed.
Sure, but the scientists doing those kinds of workflows don’t have anywhere near the money to burn on GPUs. Even before they had all of their funding cut off for being to gay or brown or whatever crap the Nazis have come up with.
This is just a small part of the perpetual cycle of growth and contraction. Growth comes from breakthroughs and innovations. Contraction comes from mis-allocation of resources and the need to extract efficiency from the breakthrough and innovation.
So now everything is booming and growing. This will slow down and if it becomes efficient enough it will remain useful and accessible. If not, it will be discarded and another breakthrough will take its place.
The gargantuan computational cost of running the machine learning processing that is now required for protein folding and molecular docking is not.
Sure but do you need the absolute gargantuan capacity that is being built right now for that? if so, for how long and at what cost?
The point is not that GPU per se are of little value… the point is that what would you do with 10,000 rocket ships if you only have 1000 projects that may be able to use them? and what can those projects actually pay? can they cover the cost of the 10,000 rockets you built?
Like Nividia which… Oh all based on AI revenue.
And even though NVIDIA is better place as they do produce something, but the something in play has little value out of the AI bubble.
NVIDIA could be left holding the bag on a super increased capacity to produce something that nobody wants anymore (or at least nowhere near at the levels we have now) so they are still very much exposed.
You’re delusional if you think GPUs are of little value. LLMs and fancy image generation are a bubble.
The gargantuan computational cost of running the machine learning processing that is now required for protein folding and molecular docking is not.
Sure, but the scientists doing those kinds of workflows don’t have anywhere near the money to burn on GPUs. Even before they had all of their funding cut off for being to gay or brown or whatever crap the Nazis have come up with.
This is just a small part of the perpetual cycle of growth and contraction. Growth comes from breakthroughs and innovations. Contraction comes from mis-allocation of resources and the need to extract efficiency from the breakthrough and innovation.
So now everything is booming and growing. This will slow down and if it becomes efficient enough it will remain useful and accessible. If not, it will be discarded and another breakthrough will take its place.
Sure but do you need the absolute gargantuan capacity that is being built right now for that? if so, for how long and at what cost?
The point is not that GPU per se are of little value… the point is that what would you do with 10,000 rocket ships if you only have 1000 projects that may be able to use them? and what can those projects actually pay? can they cover the cost of the 10,000 rockets you built?
I want cheap GPUs at home please!
I’d love this, but not Nvidia.
me too, but the GPU used for AI are not the same as what we would use at home.
maybe the factories can produce both kinds and they would be cheaper, but it is speculation at this point
They’re the ones selling shovels in this gold rush, though.