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If open weight models are the future, U.S. AI companies are going to have a hard time
(www.fastcompany.com)
this post was submitted on 26 Jul 2026
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And Llama and Mistral are ancient history at this point.
The cutting edge of local is lightyears better now. It's basically where ChatGPT/Anthropic were not that long ago, with a bit less world knowledge because of the size.
What's the cutting edge now? Skool me...I want to try it. Can I grab one using ollama?
https://sleepingrobots.com/dreams/stop-using-ollama/
And this is just the tip of the iceberg for ollama. They're the same kind of scammy tech bros as OpenAI.
The best setup depends on your hardware. There is no "easy button" unfortunately, quantized LLMs are just too intense and finicky to run without making some informed choices.
It also depends on what you want to do with the LLM. For example, some are too slow or bad at long context for agenic use, some quantizations are great at scripts but terrible outside that, or vice versa.
But LM Studio and Qwen 3.5 35B Q4 is probably the "easiest" flat recommendation I can make.
Or... honestly, just pay $40 for basically unlimited usage for a year from an API, then roll your own frontend.
Well that sucks. I was really impressed as a novice to open weight LLMs with the ease of use for Ollama on Bazzite.
I've been running LM Studio on Bazzite and I had to do nothing to get it working. Just go to the LM Studio website and download the
.appimagefor Linux. If you open it with Gear Lever it will install like an app from the app store and show up in your launcher with an icon.From there I have just been able to download models and use them from in the app. In fact I setup a local server to connect to my IDE and have been trying out local models for coding. It's pretty cool
Awesome! I'll give it a try.
I can also vouch for lmstudio. If you can get Hermes running on Linux I would suggest trying that as well. It connects to lm studio and you use Hermes to communicate with the model. Iook into it as there's a lot to it, I've really been enjoying using it so far it even learns how I like to create tasks and I've stopped having to ask it to delegate certain tasks, it just knows to do it and to break down the tasks so my fairly context starved local model can handle it.
As for a model, the Qwen 3.6 family of models do really well. I'd suggest the Qwen 3.6 35B a3b probably Q4 depending on your hardware. It's large, but because it's a mixture of experts model only 3b of experts are kept on vram at any one time so it stays fast. Qwen 3.6 27b is the smarter "dense" model, but trying to stay with Q4 for quality it becomes too large for 16GB vram and for me runs at like 2 tokens per second lol
+1 for Hermes.
If you have a newer Nvidia GPU, you can run Qwen 27B via exllamav3 and get good quality/speed in 16GB. And it's worth the trouble, as 27B is an amazing model.
If it's AMD, yeah, a3B is a good bet, depending on how much spare CPU RAM you have.
Thanks for the tip! I do have a 5080 so I'll have to look up exllamav3
You want this one:
https://huggingface.co/turboderp/Qwen3.6-27B-exl3_3.30bpw/tree/main
Or maybe the 3.5bpw one if you don’t mind less context, or 3bpw if you need more:
https://huggingface.co/turboderp/Qwen3.6-27B-exl3
For faster inference at the cost of a little more VRAM usage:
https://huggingface.co/turboderp/Qwen3.6-27B-DFlash-exl3
And you run those in:
https://github.com/theroyallab/tabbyAPI
And FYI, if you have 64GB of RAM or more, you might consider hybrid inference instead.
Thank you for all the links! Much appreciated :)