We're releasing new Qwen3.8-27B GGUFs with Unsloth Dynamic V3.1! Expect 5-10% accuracy gains on the same size vs UD3 and other quants.
We do better vs GSQ-RCO, Exllama-v3 on the pareto frontier for Terminal Bench, DeepSWE and more. Quants above 2-bit to 6-bit are updated.
We did not update 3-bit quants from UD3 to UD3.1 as we found UD3 to perform on par with 3.1.
We also decided to benchmark Exllama3 quants as the community requested them. Exllama3 keeps the token embedding layer in BF16, which is different from ISTA and UD3 / UD3.1 and GGUFs.
We decided to exclude the token embedding from all plots. This does not mean the RAM and disk space usage of EXL3 is less - it's in fact more if you include the BF16 embedding layer. We provide plots for actual RAM+VRAM usage at https://unsloth.ai/docs/models/qwen3.8#exllama-v3-considerations, and it shows exl3 quants doing much worse.
For LiveCodeBench, GSQ-RCO showed Unsloth UD3 quants doing consistently worse than ISTA's. However we found the opposite - UD3 quants (our OLD quants) do better than ISTA on LCB on all sizes except the smallest. UD3.1 further improves on LCB as well.
We approximate that GSQ-RCO used a context budget of 96K, as that best fits the data for the runs we have. At 96K, GSQ-RCO does somewhat do better than UD3, but at 128K, UD3 does much better (and UD3.1 even more)
For ISTA GSQ-RCO's AIME25 benchmarks, their quants reach 100%, 100%, 100% and 96.7% accuracy recovery.
We estimate ISTA only did 1 trial - we did 14 trials, and we show on average the recovery rae is 93% to 96%. And in fact our UD3 old quants do better on average than their quants on 14 runs vs 1 run of theirs.
We are still improving our algorithms to make smaller quants and larger quants (and also 3-bit) much more accurate, and will definitely apply these methods to Qwen4!
All blog details at https://unsloth.ai/docs/models/qwen3.8#dynamic-v3.1-update-november-11th-2026 and the new quants are at https://huggingface.co/unsloth/Qwen3.8-27B-GGUF