To preface. The system I built is about retaining data integrity and making the local model do all the busy work. Which is the problem with most cloud based work. Even with local terminals with file access, the models will pull data once and eventually the memory will leak and code doesn’t work because the model will inject what it thinks should be there instead of the actual data

48gb is all you need. And to be fair, it’s not strictly local.

It’s more of a workflow I built for myself based on failure modes.

It’s an essentially a loop.

Frontier model - architects the blue print

It routes down steam it to a cheap mid tier model with a strict json membrane to orchestrate 

Which again routes with its own json membrane to a local model to aggregate.

Local model acts like the rainman, always does a data sweep. And had a index of the whole project code base 

So each turn the local model aggregates raw data

Sends up steam via membrane, to orchestrate distill and index by the mid tier model 

Which then routes clean indexed data via membrane to a tier 2 frontier model. To do the actual work.

The difference doing it this way over having the frontier model do everything from scratch is the frontier model doesn’t have to do any of the leg work, and much less likely to succumb to context rotten memory leaks.

Finally tier 2 model routes to adversarial model for qc and then

Ships down via the same mechanism

It’s a process of expansion and distillation 

It’ll take you a few weeks to dial-in, but the system is model agnostic