I don’t know if others would find this useful, but previous did have custom harnesses etc.. but tools have improved so much that I drastically simplified.

That said, even the foundational models fail at the hard parts of my code so I use it opportunistically.

I have reduced down to just using zed, will three locally hosted models.

Qwen 3.6 27b on 1x3090 llama.cpp with 128k context ~50tps

Qwen 3.6 35B-A3B on 1x titan v + 2x1080ti llama.cpp with full context ~30tps

GPT-OSS 120b on pure cpu (slow)

I just use zeds parallel agents, task switching, stopping and fixing the code when a model gets stuck.

This still lets me stay engaged, and to modify code to be maintainable etc…

It gets me 80% there and I use to keep a subscription but often times just using googles AI mode is just as good.

That said I have 30 years of experience and insist on knowing how my code works, so this gets me 80% of the short term benefits while not depending on a 3rd party to keep my code moving forward.

Your mileage will vary and 2*5060ti 16gb cards would get around 100/tps with Qwen 3.6 35B-A3B on cards that are widely available.

To be honest the more modern cloud models are using draft tokens etc… that while they are superior for common coding tasks are degrading with more domain specific tasks.

That is just the cost of the draft model being ~10-20% of the foundation models size, and even the biggest Blackwell GPU is limited to ~250/tps so MoE or draft models are required for scaling performance at the foundational level IMHO.

The hard part is my use case are the OOD or small examples in corpus level, the above hurts there.

A Lamborghini may be nice, but I personally need a minivan more.

You wouldn't get 100 tps on a Qwen 3.6 35b with a 5060 (or two) when a 5090 can barely reach that.