Model Releases
I benchmarked 21 Qwen3.8 27B variants on 16GB VRAM
After Qwen3.8 27B came out, I decided to benchmark the models that could fit in my GPU (RTX 5080) on my actual code (C code), the results were not completely unexpected but some quants were definitely
After Qwen3.8 27B came out, I decided to benchmark the models that could fit in my GPU (RTX 5080) on my actual code (C code), the results were not completely unexpected but some quants were definitely underwhelming. TLDR: Best overall: bartowski/Qwen3.8-27B-IQ4_XS. Best uncensored: huihui-ai/Huihui-Qwen3.8-27B-abliterated-UD-IQ4_XS. For a bit more context: jpetrina/Qwen3.8-27B-IQ4_XS-pure or uncensored: Bucoid/Qwen3.8-27B-Uncensored-IQ4_XS_4BPW (sorted by Mean KLD) Model Mean KLD Same top p GGUF size sdkyuan/qwen38-27b-qat-q2_0 0.893177 ± 0.006948 85.727 ± 0.110 % 8.2GiB ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-IQ2_XS.gguf 0.767174 ± 0.006291 86.166 ± 0.108 % 7.8GiB ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-IQ2_S 0.512614 ± 0.004909 88.802 ± 0.099 % 8.6GiB empero-ai/Qwen3.8-27B-Ridge-3.7bpw 0.475767 ± 0.004483 89.612 ± 0.096 % 11.7GiB ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-IQ3_XXS 0.379222 ± 0.003992 90.270 ± 0.093 % 9.4GiB unsloth/Qwen3.8-27B-UD-Q2_K_XL (UD2) 0.350861 ± 0.003745 90.626 ± 0.091 % 9.9GiB unsloth/Qwen3.8-27B-UD-IQ3_XXS (UD2) 0.268594 ± 0.002971 91.951 ± 0.085 % 11.1GiB DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-NEO-MTP-IQ3_M 0.251270 ± 0.002702 92.315 ± 0.083 % 13.5GiB esatapedico/Qwen3.8-27B-NVFP4-MTP-LOW 0.220796 ± 0.002631 92.339 ± 0.083 % 14.5GiB mudler/Qwen3.8-27B-APEX-I-Mini 0.190209 ± 0.002354 93.012 ± 0.080 % 13.0GiB jrell/Qwen3.8-27B-i1-IQ4_XS-GGUF-Smaller 0.194459 ± 0.002242 93.049 ± 0.080 % 12.6GiB orcarouter/Qwen3.8-27B-Uncensored-Q3_K_L 0.192312 ± 0.002294 92.726 ± 0.081 % 13.6GiB unsloth/Qwen3.8-27B-UD-Q3_K_XL (UD2) 0.147186 ± 0.001809 93.734 ± 0.076 % 12.5GiB unsloth/Qwen3.8-27B-UD-Q3_K_XL (UD3) 0.142647 ± 0.001860 93.789 ± 0.076 % 12.2GiB Bucoid/Qwen3.8-27B-Uncensored-IQ4_XS_4BPW 0.091447 ± 0.001261 94.774 ± 0.070 % 13.0GiB huihui-ai/Huihui-Qwen3.8-27B-abliterated-UD-IQ4_XS 0.082871 ± 0.001205 94.981 ± 0.068 % 13.4GiB unsloth/Qwen3.8-27B-UD-IQ4_XS (UD3) 0.075626 ± 0.001097 95.258 ± 0.067 % 13.3GiB jpetrina/Qwen3.8-27B-IQ4_XS-pure 0.061984 ± 0.000917 95.551 ± 0.065 % 13.5GiB bartowski/Qwen3.8-27B-IQ4_XS 0.056482 ± 0.000856 95.835 ± 0.063 % 14.5GiB unsloth/Qwen3.8-27B-UD-Q4_K_XL (UD3) (can't fit) 0.029844 ± 0.000476 96.921 ± 0.054 % 16.4GiB unsloth/Qwen3.8-27B-UD-Q4_K_XL (UD2) (can't fit) 0.028026 ± 0.000432 96.988 ± 0.054 % 16.7GiB graph by u/Tall_Abrocoma_3533 Hope this helps other VRAM starved people like me :) submitted by /u/Storterald [link] [comments]
Related
- Optimizing Qwen3.6 / Qwen3.8-27B on 16GB VRAM: Complete Benchmark Results and Setup Guide (~30-50tps at 32k to 72k context)
- Qwen 3.8 27b is strong even at Q3_xxs
- Am I doing something wrong? Qwen 3.8 27B seems useless for agentic coding
- After pushing 1M+ tokens through Qwen 3.8 27B, here is my optimal llama.cpp config for 16GB VRAM (73k Context, Agentic Coding)
Source: r/LocalLLaMA | 2026-09-04