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Open your laptop's spec sheet and you will find a CPU, a GPU, and if it is recent enough, an NPU.
Open a cloud instance page and you get offered GPUs, TPUs, and a networking card that turns out to be a whole computer wearing a trench coat.
The obvious question, and the one I kept dodging for years: why not just build one really good processor and stop?
The short answer is that "computation" is not one thing. The longer answer is the fun part.
It runs your OS, your API handler, your regex, your if statements, your database, your build tool, and the Slack client that eats four gigs of RAM for no reason.
A CPU is built to be good at unpredictable work. Branchy code where the next instruction depends on the last result.
Pointer chasing. Long dependency chains where nothing can be done in parallel because step 5 genuinely needs the output of step 4.
To pull that off, a modern CPU core is stuffed with machinery that has nothing to do with math: branch predictors guessing which way your if goes, out-of-order execution reordering your instructions behind your back, and a cache hierarchy doing everything it can to hide the fact that RAM is embarrassingly far away.
All that cleverness costs transistors and power. So you get a handful of very smart cores, not thousands of them.
Say you need to do the same operation across a few million values. Same instruction, different data, no branches, nobody waiting on anybody.
A CPU can do this. It will even do it in parallel across its cores and its SIMD units.
It just will not do it well, because you are paying for all that branch prediction hardware to predict a branch that does not exist.
A single 4K frame is roughly 8.3 million pixels, and at 60fps you are shading all of them 60 times a second, with the same lighting math applied to each one.
So GPU designers made the opposite trade from CPU designers. Drop the fancy per-core intelligence, spend the transistors on arithmetic units instead, and run thousands of threads in lockstep.
The punchline nobody planned: neural networks turned out to be the same shape as graphics.
A forward pass through a transformer is mostly enormous matrix multiplications, which is millions of independent multiply-and-add operations. That is a GPU's native language.
Graphics cards accidentally became the substrate for the entire AI industry, which is possibly the most profitable accident in hardware history.