For those who haven’t seen this discussion before, I feel like doing the next step in the dance. Cheers Plex.
It’s important to note that nuclear is capable of satisfying baseload demand, which is particularly important for things like a commercial AI model training facility, which will be scheduled to run at full blast for multiple nines.
Solar+storage is considerably more unreliable than a local power plant (be it coal, gas, hydro, or nuclear). I have solar panels in an area that gets wildfire smoke (i.e. soon to be the entire planet), and visible smoke in the air effectively nullifies solar.
Solar is fantastic for covering the amount of load that is correlated with insolation: for example colocated with facilities that use air-conditioning (which do include data centers, but the processing is driving the power there).
While you are right about baseload being more satisfiable through nuclear, you are wrong that it’s in any way important for AI model training. This is one of the best uses for solar energy: you train while you have lots of energy, and you pause training while you don’t. Baseload is important for things that absolutely need to get done (e.g. powering machines in hospitals), or for things that have a high startup cost (e.g. furnaces). AI model training is the opposite of both, so baseload isn’t relevant at all.
It’s not life-critical but it is financially-critical to the company. You aren’t going to build a project on the scale of a data center that is capable of running 24/7 and not run it as much as possible.
That equipment is expensive, and has a relatively short useful lifespan even if not running.
This is why tire factories and refineries run three shifts, this isn’t a phenomenon unique to data centers.
What are you trying to imply? That training Transformer models necessarily needs to be a continuous process? You know it’s pretty easy to stop and continue training, right?
I don’t know why people keep commenting in spaces they’ve never worked in.
No datacenter is shutting off of a leg, hall, row, or rack because “We have enough data, guys.” Maybe at your university server room where CS majors are interning.
For those who haven’t seen this discussion before, I feel like doing the next step in the dance. Cheers Plex.
It’s important to note that nuclear is capable of satisfying baseload demand, which is particularly important for things like a commercial AI model training facility, which will be scheduled to run at full blast for multiple nines.
Solar+storage is considerably more unreliable than a local power plant (be it coal, gas, hydro, or nuclear). I have solar panels in an area that gets wildfire smoke (i.e. soon to be the entire planet), and visible smoke in the air effectively nullifies solar.
Solar is fantastic for covering the amount of load that is correlated with insolation: for example colocated with facilities that use air-conditioning (which do include data centers, but the processing is driving the power there).
While you are right about baseload being more satisfiable through nuclear, you are wrong that it’s in any way important for AI model training. This is one of the best uses for solar energy: you train while you have lots of energy, and you pause training while you don’t. Baseload is important for things that absolutely need to get done (e.g. powering machines in hospitals), or for things that have a high startup cost (e.g. furnaces). AI model training is the opposite of both, so baseload isn’t relevant at all.
It’s not life-critical but it is financially-critical to the company. You aren’t going to build a project on the scale of a data center that is capable of running 24/7 and not run it as much as possible.
That equipment is expensive, and has a relatively short useful lifespan even if not running.
This is why tire factories and refineries run three shifts, this isn’t a phenomenon unique to data centers.
“And you pause training while you dont.” lmao I don’t know why people keep giving advice in spaces they’ve never worked in.
What are you trying to imply? That training Transformer models necessarily needs to be a continuous process? You know it’s pretty easy to stop and continue training, right?
I don’t know why people keep commenting in spaces they’ve never worked in.
No datacenter is shutting off of a leg, hall, row, or rack because “We have enough data, guys.” Maybe at your university server room where CS majors are interning.