For those hearing about Cerebras for the first time, they make a chipset that's similar to a GPU in matrix multiplication speed but way bigger (a whole wafer) so it can fit more transistors and memory onto one chip. They achieve this small LOC count because they don't need to shard across multiple devices / backprop consolidate on a central CPU / etc. These tricks are usually what blows up a project from a single architecture proof of concept to a robust training pipeline that can handle the billions of parameters on modern models. This is more akin to training a whole model on a single GPU because... it kind of is.

Even with a wafer scale chipset this approach has limits. You eventually will still need to shard to fit more parameters / use different training modalities / etc. I'd look at this more as a proof of concept for the ergonomics of what LLM training can look like when you have access to a much larger compute primitive versus a new state of the art in feature-equivalent clean code.

Disclaimer: I'm a small investor in Cerebras.