I remain steadfast on the idea of “on-premise” computing solution for running AI/algorithms etc. There was an interesting write up here.
A bit of a historical context:
Along the way, the workstation companies consolidated (Apollo and eventually DEC got absorbed into HP; MIPS into SGI) or disappeared altogether (Sun became Oracle Hardware; SGI went bankrupt and sold its assets to sgi; Symbolics did similar—incidentally Symbolics was the first company with a .com domain). IBM long ago stopped even making its own brand PCs, and the news of its split means that there are now very few workstation companies trading in the same form they had “back in the day”. The only ones I can think of that have not had major changes to their corporate structures are Xerox and Sony, whose management may not even have known that they sold workstations.
This is something I agreed with instantly:
The general purpose hardware vendors want us to believe that an okay-at-anything computer is the best for everything: you don’t need a truck, so here’s a car. But when you’re hauling a ton of goods, you’ll find it cheaper and more satisfying to shell out more for a truck. Okay-at-anything is good for nothing.
It is essential to understand and devolve needs for any program running machine learning within an institution. The cloud computing costs can quickly escalate without a real “ROI” on that.