Unsloth Qwen3.8-27B Quantizations – Dynamic v3.1 Update

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The Details

Qwen3.8-27B is a 27B parameter dense model Qwen, featuring vision and reasoning capabilities, a 256K context window, and runs locally on 17GB RAM/VRAM setups. Qwen3.8 excels at agentic coding, vision and chat tasks, and can now run via Unsloth GGUFs, NVFP4 and Unsloth Desktop.

Qwen3.8-27B GGUFs now use

Unsloth Dynamic V3.1 which improves average accuracy by 5-10% accuracy at comparable sizes including Terminal Bench, DeepSWE and more benchmarks. We're working on an even better version of Dynamic quants which will be applied to Qwen4 and future models.

• Run Qwen3.8 Guide
• Download Unsloth
• Thank you Qwen for day zero access. Unsloth quants also include:
• Developer Role Support for agentic tools like Codex
• MTP enabled for fast inference
• Tool calling:
• Improved parsing nested objects to make tools succeed more Unsloth quants:
• Qwen3.8-
• 27B
• -GGUF
• Dynamic 4-bit Qwen3.8-27B in Unsloth Desktop
• Dynamic V3.1 Update (November 11th 2026)

We're excited to release new Qwen3.8-27B GGUFs using our updated Unsloth Dynamic V3.1 (preview) algorithm which improves Unsloth quants by 5-10% accuracy at the same size on most benchmarks and is the best in class for most tasks including coding, agentic workloads and general chat.

Most quants from 2bit until 6bit are updated. We found 3bit UD3 quants to do on par with UD3.1, so we do not update them. We are actively working on making all quants better!

We ran agentic benchmarks including Terminal Bench 2.1, DeepSWE v1.1, and general benchmarks like LiveCodeBench v6, GPQA Diamond, MMLU-Pro, AIME25 and many others. We used 256K context lengths for all agentic benchmarks, and 128K for the rest. We followed the same methodology for Qwen3.8-27B as DeepSeek-V4.1-Flash and Qwen3.8-Flash-Next which also utilized our V3.1 preview algorithm.

• Dynamic V3.1 Full Breakdown
• Click below to see full details of benchmarking and more:
• Click to expand Full details and breakdown
• Exllama-v3 considerations

To compare GSQ-RCO and Exllama-v3, we had to exclude the token embedding layer from the x axis, as Exllama-v3 uses BF16 for it.

If you consider the full download size and total RAM usage for Exllama-v3, then adding the BF16 embedding table and including all embedding sizes changes the plots. Our main plots's x axis removes the embedding layer sizes from all quant providers.

However on pure RAM usage and disk space usage vs accuracy, the below plot is the true reflection of actual usage.

We have not investigated why Exllama-v3 keeps token embeddings in BF16, but as the lm_head is still quantized heavily, the embedding layer might have "absorbed" most of the quantization damage from it. We would be glad to benchmark new Exllama-v3 non BF16 token embedding quants like using 8-bit for example, and see if benchmarks change.

GSQ-RCO results are misleading

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.

On another benchmark, for ISTA GSQ-RCO's AIME25 benchmarks, their quants reach 100%, 100%, 100% and 96.7% accuracy recovery. And they said our quants do worse.

Through out investigations, we estimate ISTA only did 1 trial - we did 14 trials, and we show on average the recovery rate is 93% to 96% - not 100% accuracy recovery.

And in fact our UD3 old quants do BETTER on average than their quants on 14 runs vs 1 run of theirs, so we draw the opposite conclusion if you run the trials more.

We also checked and graded our answers using Math Verify Dynamic 3.1 GGUFs and also LightEval and we find exactly the same conclusions. We also tried greedy decoding (temperature of 0), and this could not get the 100% accuracy recovery ISTA found across multiple greedy runs, as GPUs themselves are non deterministic. That is why you benchmarks should be run multiple times. Greedy decoding is also not representative of real world use cases.

We also checked 96K (which is most likely ISTA's budget) and 128K, and our quants still do on average better.

KLD of Unsloth v3.1 vs v3

We did KLD for multiple domains for Unsloth UD3.1 quants and UD3 quants - we reduce KLD throughout most quants and most domains - we did not replace the 3-bit ones, as we found UD3 to do mostly great, and the accuracy recovery on benchmarks was not significant enough with UD3.1 to warrant an update, so we did not update them.

Benchmark details and setups

We follow the same procedure for all benchmarks as for Qwen3.8-Flash-Next, DeepSeek-V4.1-Flash as well as discussed in Dynamic 3.1 GGUFs. We used temperature 1.0, top_p 0.95, top_k 20, min_p 0, no presence or repetition penalty, preserve_thinking and xhigh reasoning for Qwen3.8-27B.

• ⚙️
• Usage Guide
• Qwen3.8-27B Requirements:
• Qwen3.8-
• 27B

4-bit quants work on 16-19GB VRAM like RTX 5080, 4090 or a Mac with 24GB RAM.

• Table: Hardware requirements
• (units = total memory: RAM + VRAM, or unified memory)
• 1-bit
• 2-bit
• 3-bit
• 4-bit
• 6-bit
• 8-bit
• BF16
• 7-8 GB
• 9-11 GB
• 12-14 GB
• 16-19 GB
• 23-26 GB
• 31 GB
• 56 GB
• If you want to use

MTP for faster inference, prepare to have 1-2GB extra headroom.