> Now at 40-50tok/s generation and ~2000 tok/s

Not clear how you went from ~11-14 to ~40-50 tok/s. Is it by running the quant native model and adding a second spark?

Cheers

Well, if you are making side-hustle money now using online models that, critically, you could also run at home, then it sounds like it’s just a matter of numbers. Oh and, unless you spend a lot more than 5k, your local model will still be slower than the online model. What’s your estimated ROI?

Assuming that’s not true based on your phrasing, you’d be shooting yourself in the foot. Start using online models with the same quant at least benchmark as what you could run at home. Prepare for the at home model to be slower.

You probably want to try renting some time on a dedicated box with roughly the specs you’re considering and running the open models for a bit to see if you would actually use them before dropping a lot on local hardware. A 128 gig MacBook Pro isn’t going to get you an amazing model, and certainly not amazing speed. GLM 5.2 wants something like 350+ gigs at fp4 iirc.

Highy anecdotal: I have tried various self-hosted models using both vllm and llama.cpp. I am in a situation where I have access to large amount of memory (~320 GB).

While experimenting with quantization I found that there is a non-trivial tradeoff between quality and memory footprint. Overall my experience follows the reported pattern of "2-bit is mwah, 4-bit half decent and 6-bit required for programming. Still, although MiniMax-m2.7 is useable with the 6-bit quantizations that unsloth provides, it felt like such a breath of fresh air when I used the reference full-size model.

I find it difficult to say why. I had mostly the same setup as before (parsing had to be slightly adjusted in Zed). Aside from not experiencing the thinking loops (where minimax would get stuck generating the same sentences over and over) there is little evidence of any real improvement (although the average thinking time felt shorter).

I would recommend against very low quantizations of GLM 5.0/5.1/5.2 or Kimi 2.5/2.6. Smaller models were more reliable, and therefore more useful.

I have tried llama-cpp, vllm is nicer (ray, handles queueing, doesn't have the cache invalidation bug for qwen/gemma models) and unsloth has toxic employees in their discord.

I've run 2 qwen/gemma @8bit with full context window side-by-side. Right now I have 4 models on my spark (qwen36moe, embedding, reranker, qwen3-1.7B) to support my markdown kb tool.

The setup is not as capable, but still good and gets better with models/algos. To me, it's more about the freedom to tinker, freedom from token bill anxiety, and potential right to compute should the government/oligarchy decides it gets to decide who can access which models.

So far there aren't any open weight model releases for the Qwen 3.7 family.

Can you try and tune your Claude or whatever LLM you're using for your text to phrase things in plain English. Way less use of antithesis, at least. You can probably find a skill for it, if not get an LLM to write your own.