No. You cannot. It's the wrong tool for the problem.

That little "add" of yours has the overhead of: having an LLM emit it as a tool call, having to pause the LLM inference while waiting for it to resolve, then having to encode the result as a token to feed it back.

At the same time, a "transformer-native" addition circuit? Can be executed within a single forward pass at a trivial cost, generate transformer-native representations, operate both in prefill and in autoregressive generation, and more. It's cheaper.

"smallest supercomputing cluster that can add two numbers"

I mean, yeah, no need to put a bunch of high powered cars in a circular track to watch them race really close to each other at incredible speeds, causing various hazards, either. Especially since city buses have been around for ages.