As someone in the comments said we made a separate Q4_0 MTP if that's helpful so you can use that.
But I would suggest using UD-IQ3_XXS for 10.9GB for 16GB machines or Q2_K_XL
And I had similar experiences when Qwen3.8-27B unsloth images just came out with the full Q8_K_XL, I'm using an AMD setup which has modifications to save to disk the kv, but your (assuming you are part of the unsloth team) for some reason have been giving me similar issues.
I tried https://huggingface.co/mradermacher/Qwen3.8-27B-Uncensored-G... the 8 bit, 6 and 2 bit... the 2 bit almost use the complete KV doing it's thing and didn't loop itself.
It can be something in my setup, there is a very high chance of that, but the previous 3.6 images from qwen, the 27B, the 31A3 and 122 they are all unsloth and did work on my setup without issues...
Again could be my setup... let me know if there is any data I can supply to you to debug if needed.
Often times I run into issues like this it’s because I am using settings for a different model or just forget to set them up.
For those of us with a 16GB GPU, how do they compare with ExllamaV4 at 4-bit (4.0bpw)?
It looks like that fits in 12.5GB of VRAM since embedding are left in DRAM, Unsloth Studio and other llama.cpp derivatives have to load these weights in VRAM for tied embedding models like Qwen3.8.
ExllamaV3 4.0bpw fits in 12.5G of VRAM and beats IQ4_XS according to the measurements here: [turboderp/Qwen3.8-27B-exl3](https://huggingface.co/turboderp/Qwen3.8-27B-exl3)
But those were compared against UD2.0 I guess. Also plans to support these (SOTA) quants in Unsloth Studio?