GoKawiilDynamic v3.0 is the next iteration of our Dynamic quantization and a major improvement over Dynamic v2.0.
Today, we’re releasing Dynamic v3.0 quants that deliver >10% top-1% better accuracy at the same size compared to every other provider. This is an update of our first shared early preview version of Dynamic v3.0. The new 3.0 GGUFs work with most inference engines including llama.cpp and .
Dynamic v3.0 overall preserves more model quality while keeping the same size, with stronger results across metrics like Divergence-300 @32 and KL Divergence. Also a huge thanks to all your support! We saw over 5.1 million Unsloth Qwen3.8 downloads in just 5 days! See below for more graphs/benchmarks and analysis
Our new methodology composes of many new features and improvements. We now use a much higher-quality imatrix calibration dataset from diverse sources. The dataset is refined for agentic coding, chat, and multilingual performance. We also improved layer selection and introduced many more quantization techniques to preserve as much model quality as possible.
We do not train on the imatrix calibration dataset, and we do NOT use QAT or QAD. Everything is done through post-training quantization. Our imatrix file used is available for the community to test, evaluate, and use. We encourage researchers and developers to create variations and fine-tunes of Qwen3.8 using our Unsloth quants/imatrix. You can read our as well.
We also removed the MTP module from smaller quants under UD-Q2_K_XL (8.37GB and lower) to converse around 500MB of disk space - you can use the Q4_0 MTP separate module if needed
We also made some smaller UD-1bit quants with UD-IQ1_S being 6.2GB (without MTP) which retain around 72% top-1% accuracy yet being 89% smaller.
UD-Q2_K_XL is around +8% more accurate on top-1% than the next best and it's 9.83GB and managed to create a working HTML program with 1 small JS bug - previously it would break.
We generally report top-1% accuracy like how for Kimi-K3 "Dynamic 1-bit reaches ~78.9% top-1 accuracy while being 62% smaller." However top-1% is an argmax on 1 prediction, so it's not really effective on gauging actual inference.
We created a dataset of 300 held out examples (NOT in calibration dataset) from Terminal-Bench 2.1 + DeepSWE + Harbor + MathArena 2025-26 + non-Latin/long-doc prompts and we did greedy argmax decoding for 32 tokens for BF16 vs all quants and providers. See for more details on overfitting.
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