Matrix multiplication is the clean math story. The machine doing it is messier. Charges move. Transistors switch. Bits travel through memory hierarchies and across interconnects. Power gets delivered. Heat has to leave. If you ignore that physical machine, you will miss why real AI systems behave the way they do.
Take huge piles of numbers and produce outputs fast enough to train or run modern models.
Real work happens through switching devices, charge movement, signaling, storage, and transport across physical hardware.
Getting operands to the right place at the right time is often harder than doing the multiply-add itself.
Every watt spent moving and switching information has to be delivered and then removed as heat.
The math is not the machine. The machine is a choreography of switching, transport, synchronization, power delivery, and thermal limits. AI performance lives inside that choreography.
During training or inference, the system keeps doing a simple-looking thing at huge scale: multiply numbers, accumulate results, apply nonlinearities, move tensors, repeat. The software view says this is linear algebra. Fair. But incomplete.
GPUs are built to perform vast amounts of similar work in parallel. They are good when the same kind of operation needs to happen many times over large arrays of data. That is why AI landed on them so hard.
At the bottom, semiconductor devices switch states and control current flow. Logic is embodied in physical switching behavior, not floating math symbols.
Registers, caches, SRAM, DRAM. Different storage layers trade speed, density, distance, and energy. That hierarchy shapes what the chip can feed into compute units.
Signals move through wires, traces, packages, memory channels, and board-level links. Every hop has latency, bandwidth limits, integrity issues, and energy cost.
A fused multiply-add unit can be extremely fast. But if data is late, the expensive compute hardware waits. So system designers obsess over locality, caching, tiling, batching, and reuse. They are fighting the cost of transport.
Real performance often depends less on the raw math capability and more on whether the system can keep the compute units supplied with data from nearby memory rather than far-away memory.
High-performance compute needs stable power at large scale. Delivering that power cleanly is part of the system design problem.
Switching and transport consume energy. Energy turns into heat. If heat cannot leave fast enough, clocks, density, packaging, and total sustained performance all get squeezed.
Now infrastructure matters too: rack power, cooling design, system packaging, and datacenter constraints become part of the AI story.
You stop asking only “how many FLOPS?” and start asking “where are the operands coming from, what is the memory path, how much energy does this transport cost, and what happens thermally when we sustain this workload?”
The workload asks for linear algebra.
The chip negotiates that request through switching, storage, transport, and timing.
Data movement keeps charging you, in latency, bandwidth pressure, and energy.
Sustained performance only exists if the system can physically survive its own activity.
Why memory movement is often harder than the compute
This should zoom in on hierarchy, locality, bandwidth, latency, reuse, and why moving data can dominate total system cost.
Why optics keeps showing up in modern systems
That expands the lens from electrical compute bottlenecks into modulation, interference, fiber, photonics, and sensing.