If you can do what you need with qwen3.6-27b, it starts to look really interesting. That model is crazy good for the size, but it's a pain tweaking the params to run it on a 4090 with decent context and decent token speed. A 5090 looks tasty from that point of view, and only more so if you think in terms of the probability of that model being roflstomped by something in the same weight class in the next couple of years. I reckon that probability is significantly non-zero, but fundamentally it's a guess.

Or you want to process private data or don’t have reliable connectivity. There are a few more reasons for local models I think.

Also, electricity isn't free.

I don't have 5090, I have 395+ and I use for gpu assisted OCR, embeddings vector, speach to text and etc. I have a freedom of using a large library of various models and I can fit a lot in 128gb.

I don't use it for coding, I have $20 Gemini, $20 codex, etc.

But then I got the framework board for $1700, now it's $2700

MoE is fine. You can put the shared weights on the 5090 (will fit handily even for the largest models) and expert weights on CPU, possibly with weights offload from storage.