Most people getting into AI training now think the hard part is getting access to huge GPUs. So they rent 8x GPU setups with insane VRAM numbers, crank everything to max power, throw random configs and random data into training, then act surprised when the model outputs complete nonsense.
The weird part is that massive compute started becoming easier to access faster than people learned evaluation, debugging, or what the model is actually learning from. A lot of training right now feels like “big GPU number = intelligence” when in reality bad decisions just scale faster on bigger hardware.
[link] [comments]