People often ask how large language models actually work. Vendors explain it with sleek diagrams, and academics explain it with equations that should probably come with a wellness check. The truth is simpler and significantly less elegant.

An LLM is, at its core, a trillion-parameter anxiety ball duct-taped to matrix multiplication and trained by grad students who haven’t slept since the pandemic. It “learns” by predicting the next token, which is the computational equivalent of finishing people’s sentences until you’re right often enough that venture capitalists applaud.

Training this kind of model requires very large GPU clusters, often hundreds for mid-sized models and thousands for the frontier systems. It also requires petabytes of text scraped from every corner of the internet and an energy footprint that feels uncomfortably close to mythological wildlife. Most of the engineering effort goes into keeping the cluster from overheating, crashing, or deciding to invent its own writing style.

The situation becomes even more interesting once you attempt to run these systems on mobile hardware. Phones were designed for messaging, navigation, and taking unflattering photos, not for hosting a trillion parameter text oracle. The result is thermal throttling, rapid battery drain, and a device that begins to feel like it is warming itself out of self-defense.