Model Releases
Compared Qwen 3.8 27B community quants on RTX 6000 vs Claude Opus 4.6
*part 2 of an earlier post: previous quant comparison with voxel island creation this time I rented three rtx pro 6000 96gb, on each one I launched a qwen 3.8 27b quant and gave them 4 identical promp
*part 2 of an earlier post: previous quant comparison with voxel island creation this time I rented three rtx pro 6000 96gb, on each one I launched a qwen 3.8 27b quant and gave them 4 identical prompts: classical pool game air hockey 1v1 battle foosball official match demonstration bowling scoring simulation my setup: each model was asked to write a single html with a self-playing 3d game, no system prompt, reasoning set to xhigh, all quants with a dflash2 drafter and I chose the best attempt from each results quant size total tokens avg. t/s atomic ad-q6_k 23.29 gib 393,089 114.17 unsloth ud-q6_k_l 22.53 gib 363,083 70.33 bartowski q6_k 21.85 gib 325,700 79.71 claude opus 4.6, subscription — 200,565 72.47 btw I put all the prompts and logs here in a github repo I'm from atomic.chat and we make quants and have an open-source app for running ai models locally (I'm a co-founder, so any feedback is appreciated, we're trying to make the product as good as possible for you guys) Atomic Dynamic Qwen 3.8 27B GGUF quants Unsloth Dynamic Qwen 3.8 27B GGUF quants Bartowski Qwen 3.8 27B GGUF quants submitted by /u/Top-Eye-8104 [link] [comments]
Related
- We quantized Qwen 3.8 27B and compared the quants on an RTX 6000
- Qwen 3.8 27B Overthinking, It has to be done, it has to be overthinking to punch Opus 4.6
- 2 x 5070ti Qwen 27B full config / stats
Source: r/LocalLLaMA | 2026-08-26