yeasah/Qwen3.8-27B-exl3 quants - lowest KLD of 0.0002

Reddit r/LocalLLaMA Models

Summary

A new EXL3 quant of Qwen3.8-27B by yeasah achieves the lowest KLD (0.0002) among competitors while requiring only 18.8 GiB of VRAM for weights, outperforming quants from Unsloth and turboderp.

Noticed this new EXL3 quant of 3.8-27B which achieves lowest KLD than anyone else and does it using only 18.8 GiB VRAM for weights. Pretty impressive numbers! https://huggingface.co/yeasah/Qwen3.8-27B-exl3 maker quant KLD mean file size yeasah 6bpw 0.0002 23 GB Unsloth Q8_K_XL 0.0005 31.5 GB Minachist INT8 AutoRound attn-bf16 0.00056 36.9 GB turboderp 6bpw SC 0.0006 23 GB
Original Article

Similar Articles

Qwen3.6-27B Quantization Benchmark

Reddit r/LocalLLaMA

This article benchmarks various Qwen3.6-27B quantizations (Q8 to Q2) using KLD and Same Top P metrics, comparing providers like Unsloth and mradermacher, and offers recommendations for quality-size trade-offs.

Qwen3.8 Flash AP Quants

Reddit r/LocalLLaMA

The post announces new quantized versions of the Qwen3.8 Flash model that outperform other high-quality quants through modified KLD measurement and optimized prefill performance.

Statistically Lossless Qwen3.8 27B GGUFs

Reddit r/LocalLLaMA

kaitchup released statistically-lossless SLQ-quantized GGUF versions of Qwen3.8-27B in three accuracy-target variants (EAR98, EAR98.5, EAR99), built by mixing donor quantized weights across 498 matrices.