The whole point is you can get equal performance from a single server instead of a ton of little ones. The ton of little ones forced them to totally re-architect to work around the massive latency between servers. A single server would have allowed them to stick with a sane architecture, and saved them millions in development time and maintenance nightmares.
It would be nice if were true, but you simply can't, there's no magic that makes an expensive server that much faster - it's just a bit faster for a lot more money. It can make sense if you only want 10x the performance and the server is cheaper than the rewrite.
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)
We're not talking about cars. We're talking about computers. 8TB of RAM is 8TB of RAM, it doesn't get better by spreading it across a thousand servers. 4096 CPU cores are 4096 CPU cores, they don't get better by spreading them across 1000 servers. Those things get worse spreading them across servers, because you massively increase the latency to access them, and for them to access shared data.
Please give an example of this "monster server" you keep talking about with 8 TB of RAM, 4096 cores, N network interface cards, and 100 TB of SSD, with a cost estimate. Otherwise we can't have a real discussion. People have a pretty good idea of how to build / what it costs to build something with 128 GB of RAM, 32 cores, a couple of NICs, and 2 TB of SSD, but what you're talking about is 50-100x beyond that.
And exactly to his point - you still can't treat these as one uniform huge memory / computational space for your application (these machines seem designed for virtualization rather than one huge application). You run into the same distributed computing issues you would with your own hardware, just with a 5/10x larger initial investment and without a huge amount of pricing control / flexibility in terms of adding capacity / dealing with failures as they arise.
Actually you can treat these as one uniform huge memory / computational space for your application. They're not meant only for virtualisation. In particular, the Oracle Database is a perfect fit for a system with thousands of cores and terabytes of memory.
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.
Just for a reference point, an appropriately spec'd big iron machine from a major supplier with 8tb of RAM will run you $10 to $20 million. There's no mainstream commercial configuration that is going to get you to 4096 cores though.
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....
Why should he give an example of a "monster server" with specs like that?
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.