Machines with terabytes of RAM do exist and get used - working well on such setups is a goal of modern JVM GCs for instance - but making a single machine that large which acts like a single machine isn't easy, nor especially desirable. One machine is a unified failure domains outside of mainframe-land, so if you had a 20k core machine with 100TB of RAM you could never reboot it to apply OS updates and it'd die all the time from failed parts.
Even once you get beyond that most software stacks use locking and stop scaling beyond a few hundred cores at best and that's assuming very heavily optimized stacks. AI workloads are easier because they're designed from scratch to be inherently parallel without lots of little locks and custom data structures all over the place like a regular computer has.
Disk storage is one of the places where you can parallelize and scale out relatively easily and you do see datacenter sized disks there.
Machines with terabytes of RAM do exist and get used - working well on such setups is a goal of modern JVM GCs for instance - but making a single machine that large which acts like a single machine isn't easy, nor especially desirable. One machine is a unified failure domains outside of mainframe-land, so if you had a 20k core machine with 100TB of RAM you could never reboot it to apply OS updates and it'd die all the time from failed parts.
Even once you get beyond that most software stacks use locking and stop scaling beyond a few hundred cores at best and that's assuming very heavily optimized stacks. AI workloads are easier because they're designed from scratch to be inherently parallel without lots of little locks and custom data structures all over the place like a regular computer has.
Disk storage is one of the places where you can parallelize and scale out relatively easily and you do see datacenter sized disks there.