We made something called Divergence-300 @32 (and later @512) which tests actual inference across 32 tokens on a held out test (Terminal Bench, DeepSWE, Math etc)
Great to hear that you are planning larger benchmarks! I am particularly interested in longer-running tasks with many steps and self-correction. Divergence is fine as long as the model can still solve the task, which Divergence-300 @32 does not measure.
The current benchmark suites that frontier AI labs use are probably a good fit, e.g.
Hey we did not remove the MTP for sizes above 8GiB - but yes for small GGUFs under 8 ish GiB, we removed the MTP module (IQ2_XXS and lower), because it's 500MiB to 750MiB in size, and on small 8 GiB machines, even 500MiB is needed.
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
Daniel, question I got the Qwen3.8-27B-UD-Q2_K_XL.gguf from https://huggingface.co/unsloth/Qwen3.8-27B-GGUF?show_file_in... and continue with my testing, but the model quickly felt into a loop of asking the same thing over and over again, I have seen the MOE do that but not the dense ones.
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.
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.
>But I would suggest using UD-IQ3_XXS for 10.9GB for 16GB machines or Q2_K_XL
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.
For high concurrency, using the blackwell's native native W4A4 MLP compute path, nvfp4 is something like a 1.2-1.5x performance increase over FP8. We're doing data enrichment (so, tasks completed successfully + tokens/second) so the performance bump shows up in the tasks/month number.
I am just now getting the benchmarks running against 3.8 27b but I expect similar results from benching 3.6 27b at the same quant.
I didn't have time to run this as well, but we're getting a 99% agent completion rate across all tasks and 98% task decision that matches the human selected option(s) on Qwen 3.8 27B @ NVFP4. There may be a difference between FP8 and NVFP4 but it's inconsequential for our data enrichment purposes.
Ok no worries - if there are any future issues - feel free to message / make a HF issue - we'll fix promptly!
Also note its best to follow Gemma4's official sampling params since they evaled with it - dry multiplier sometimes works, but it actually screws up reasoning sometimes
We do plan to do larger benchmark suites though!