this post was submitted on 12 Aug 2026
51 points (98.1% liked)

LocalLLaMA

5041 readers
14 users here now

Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.

Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.

As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.

Rules:

Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.

Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.

Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.

Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.

founded 3 years ago
MODERATORS
 

In the release page: "The other model(s) in Qwen3.8-series would be released later", we know that Qwen3.8-27B is dropping next, but that statement implies there might be other models beyond the 27B!

you are viewing a single comment's thread
view the rest of the comments
[–] domi@lemmy.secnd.me 1 points 2 days ago

128GB is plenty to try different models and see what works best. Have fun!

In case you didn't see it yet, your best friend for Strix Halo machines is https://strixhalo.wiki/

Lots of info on how to setup llama.cpp (with pre-made toolboxes), configure the BIOS settings for maximum available VRAM and Linux for the best performance.

I would recommend setting um llama-swap together with the Strix Halo Toolboxes with llama.cpp. So you can freely swap between models.