For example, if a $30k car can go 150mph, it doesn't mean a $300k car can go 1,500mph it just doesn't happen. A Bugatti Veyron goes, what? 254mph that's not even double (and it costs a lot more than $300k)
32TB RAM 1024 Cores (64 x 16 core), 928 x PCI Express I/O slots:
16TB RAM 256 cores (probably multiple threads per core), 640 x PCIe I/O adapters:
4TB RAM 256 cores (512 threads), 288 x PCIe adapters:
It's true that for some use cases, you'd be better off carving it up using some form of virtualisation, but it isn't a requirement to reap the benefits of a massive system.
Both the Solaris scheduler and virtual memory system are designed for the kind of scalability needed when working with thousands of cores and terabytes of memory.
You also don't run into the same distributed system issues when you use the system that way.
You also do actually have a fair amount of flexibility in dealing with failures as they arise. Solaris has extensive support for DR (Dynamic Reconfiguration). In short, CPUs can be hot-swapped if needed, and memory can also be removed or added dynamically.
Fujitsu's SPARC Enterprise M9000 mentioned in another reply is $5 to $10 million depending on configuration (assuming you want a high-end config).
If you go big iron with any supplier worth buying from, they will absolutely murder you on scaling from their base model up the chain to 4tb+ of memory. The price increases exponentially as others have noted.
The parent arguing in favor of big iron is completely wrong about the economics (by a factor of 5 to 10 fold). The only way to ever do big iron as referenced, would be to build the machines yourself....
He gave the argument that spreading the CPU's and memory out does not make them better.
So part of the point is that if the starting point is 8TB of RAM and 4096 cores distributed over a bunch of machines, then a "large machine" approach will require substantially less.
I've not done the maths for whether or not a "Twitter scale" app would fit on a single current generation mainframe or similar large scale "machine" (what constitutes a "machine" becomes nebulous since many of the high end setups are cabinets of drawers of cards and on persons "machine" is another persons highly integrated cluster), but it would need to include a discussion of how much a tighter integrated hardware system would reduce their actual resource requirements.
What happens when you need multiple datacenters? How about if you need to plan maintenance in a single datacenter? Therefore you need at least two servers in each datacenter, etc.
Let's say you decide to use smaller machines to serve the frontend but your backend machines are big iron. Are you going to perform all your computation and then push the data out to edge servers?
There's much more to this then loading up on memory and cores.