LocalLLaMA
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.
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27b fits easily into 128 unified RAM, even unquantized. So something was definitely wrong.
Running 3.8-27b on my Strix Halo box right now.
Hey, I also have a Strix Halo box. Which model are you running exactly? The Q8 and BF16 are half the size of Qwen3-Coder-Next-UD-Q8 I have been running before. I tested 3.8-BF16 and UD-Q8 but both run at abysmal speed in comparison to Coder Next (around 8 t/s during chat). Which one gives you best results?
You need to use MTP (Multi token prediction) to get acceptable speeds on a Strix Halo. On llama.cpp you just need to add
--spec-type draft-mtp --spec-draft-n-max 3 -np 1to your arguments.You should see around 15-20 t/s in coding tasks at Q8, which will still be much slower than Qwen3-Coder-Next-UD-Q8 since it's 27b active parameters instead of 3b.
As for Q8 vs BF16, it usually makes barely to no difference in quality. Speed is higher on Q8 though. Still testing with 3.8 since it just released.
Thank you, I will check this out. So what I got from this is that the speed may be slower but results should be more accurate?
Yes, 27b is going to produce much better results.
On a DGX with 128G unified I could push Qwen3.6-27B to 8-bit and it had pretty decent response. It just croaked with MLX on the Mac.
Waiting to see what Apple announces next month. Judging by RAM prices, it'll be waaay outside my budget.