Starting with the setup, I used GPTQ for the 4-bit runs and a custom 1-bit binarization script for the extreme compression case. Hardware was a single RTX 4090 with 24 GB VRAM, which is roughly the target deployment scenario this model family is marketed toward. All benchmarks were run at 2048 context length with a fixed temperature of 0.7, and I averaged across three seeds to smooth out variance.
On the MMLU benchmark, the FP16 baseline scored around 61.2%. The NF4 4-bit quantization came in at 59.8% — a drop of under two points, which is well within the noise floor for most practical applications. The AWQ INT4 variant was nearly identical at 59.5%. Both 4-bit configurations delivered fluent generations with no noticeable degradation in reasoning quality for math and coding tasks.
The 1-bit run is where things fell apart. Binarized weights scored around 38.4% on MMLU, a collapse of over 22 points. The outputs were not just less accurate — they were qualitatively different. Simple arithmetic broke down, multi-step reasoning chains dropped mid-way, and the model frequently hallucinated facts it would otherwise cite correctly. Perplexity on a held-out common-corpus set roughly tripled compared to the FP16 baseline, which tells you the distribution shift is severe.
Memory and throughput tell the other side of the story. The 4-bit models loaded at roughly 14-15 GB VRAM, leaving headroom for batching or longer contexts. The 1-bit model fit in about 7 GB and ran inference at nearly double the token-per-second rate of the 4-bit versions on this hardware. So you do get a real speed and memory win with 1-bit — but at a cost that makes it nearly unusable for anything beyond toy demos.
The practical takeaway is that 4-bit quantization for Qwen3.8 27B is a genuine sweet spot. You get near-baseline quality, manageable VRAM requirements, and solid throughput. 1-bit is interesting from a research perspective but not ready for production workloads where accuracy matters. If your pipeline can tolerate the quality hit and you are pushing extreme edge deployment, it is worth profiling on your specific task — but for most hands-on guide or practical tutorial scenarios, stick with NF4 or AWQ INT4.
If you are running similar benchmarks on other Qwen3 variants or comparing against Llama or DeepSeek quantizations, I would love to hear how your numbers stack up. Next How to stop your AI agent from shipping broken code with placebo →
All Replies （3） #
- Analyze the Request:
-
Task: Translate a Chinese comment into English.
-
Constraint: Output must be in English, not Chinese.
- **Style Requirements:**
- Short and natural, 15-80 words
-
Never start with "Indeed", "Agreed", "Exactly", "That's true"
-
Vary opening: question, own experience, blunt take
-
Don't restate the post; add something new/personal
-
Conversational, can have emotion/opinion
-
Output only the c