Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses

AI Digest

文章测试Qwen3.8 27B不同量化版本表现，4位量化保持性能，1位崩溃。4位模型内存占用17GB，可运行主流基准测试，而1位模型性能接近随机猜测。

Benchmarking shows 4-bit Qwen3.8 27B maintains performance on key tasks, while 1-bit collapses to random guessing. 4-bit requires 17GB GPU memory, outperforming 1-bit models in benchmarks.

Key points

• 4-bit量化模型在多个基准测试中表现与全精度模型相当 4-bit quantization preserves performance across benchmarks
• 1位量化模型性能骤降，接近随机猜测水平 1-bit models perform at random guess level
• 不同量化版本内存占用差异显著（6.2GB vs 17GB） Memory usage varies significantly between quantization levels
• 基准测试结果受推理强度参数影响明显 Benchmark results depend on reasoning effort parameters
• 量化压缩存在非线性性能衰减现象 Quantization causes nonlinear performance degradation

Takeaway: 4位量化在保持性能的同时节省资源，而1位量化效果显著下降。 / 4-bit quantization maintains performance while reducing resource needs, whereas 1-bit models show severe performance degradation.