{
  "video_id": "reddit_1w7ee1c",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "I benchmarked 21 Qwen3.8 27B variants on 16GB VRAM",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1w7ee1c/i_benchmarked_21_qwen38_27b_variants_on_16gb_vram/",
  "external_url": null,
  "upload_date": "20260904",
  "published_at": "2026-09-04T19:33:44+00:00",
  "transcript": "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.\n\n***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`\n\n*(sorted by Mean KLD)*\n\n|Model|Mean KLD|Same top p|GGUF size|\n|:-|:-|:-|:-|\n|sdkyuan/qwen38-27b-qat-q2\\_0|0.893177 ± 0.006948|85.727 ± 0.110 %|8.2GiB|\n|ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-IQ2\\_XS.gguf|0.767174 ± 0.006291|86.166 ± 0.108 %|7.8GiB|\n|ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-IQ2\\_S|0.512614 ± 0.004909|88.802 ± 0.099 %|8.6GiB|\n|empero-ai/Qwen3.8-27B-Ridge-3.7bpw|0.475767 ± 0.004483|89.612 ± 0.096 %|11.7GiB|\n|ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-IQ3\\_XXS|0.379222 ± 0.003992|90.270 ± 0.093 %|9.4GiB|\n|unsloth/Qwen3.8-27B-UD-Q2\\_K\\_XL **(UD2)**|0.350861 ± 0.003745|90.626 ± 0.091 %|9.9GiB|\n|unsloth/Qwen3.8-27B-UD-IQ3\\_XXS **(UD2)**|0.268594 ± 0.002971|91.951 ± 0.085 %|11.1GiB|\n|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|\n|esatapedico/Qwen3.8-27B-NVFP4-MTP-LOW|0.220796 ± 0.002631|92.339 ± 0.083 %|14.5GiB|\n|mudler/Qwen3.8-27B-APEX-I-Mini|0.190209 ± 0.002354|93.012 ± 0.080 %|13.0GiB|\n|jrell/Qwen3.8-27B-i1-IQ4\\_XS-GGUF-Smaller|0.194459 ± 0.002242|93.049 ± 0.080 %|12.6GiB|\n|orcarouter/Qwen3.8-27B-Uncensored-Q3\\_K\\_L|0.192312 ± 0.002294|92.726 ± 0.081 %|13.6GiB|\n|unsloth/Qwen3.8-27B-UD-Q3\\_K\\_XL **(UD2)**|0.147186 ± 0.001809|93.734 ± 0.076 %|12.5GiB|\n|unsloth/Qwen3.8-27B-UD-Q3\\_K\\_XL **(UD3)**|0.142647 ± 0.001860|93.789 ± 0.076 %|12.2GiB|\n|Bucoid/Qwen3.8-27B-Uncensored-IQ4\\_XS\\_4BPW|0.091447 ± 0.001261|94.774 ± 0.070 %|13.0GiB|\n|huihui-ai/Huihui-Qwen3.8-27B-abliterated-UD-IQ4\\_XS|0.082871 ± 0.001205|94.981 ± 0.068 %|13.4GiB|\n|unsloth/Qwen3.8-27B-UD-IQ4\\_XS **(UD3)**|0.075626 ± 0.001097|95.258 ± 0.067 %|13.3GiB|\n|jpetrina/Qwen3.8-27B-IQ4\\_XS-pure|0.061984 ± 0.000917|95.551 ± 0.065 %|13.5GiB|\n|bartowski/Qwen3.8-27B-IQ4\\_XS|0.056482 ± 0.000856|95.835 ± 0.063 %|14.5GiB|\n|unsloth/Qwen3.8-27B-UD-Q4\\_K\\_XL **(UD3)** *(can't fit)*|0.029844 ± 0.000476|96.921 ± 0.054 %|16.4GiB|\n|unsloth/Qwen3.8-27B-UD-Q4\\_K\\_XL **(UD2)** *(can't fit)*|0.028026 ± 0.000432|96.988 ± 0.054 %|16.7GiB|\n\n[graph by u\\/Tall\\_Abrocoma\\_3533](https://preview.redd.it/e1k7ao0seknh1.png?width=1313&format=png&auto=webp&s=8e413f6ed0d402cd6ac3b0bb2e095dc2e4c2b494)\n\nHope this helps other VRAM starved people like me :)\n\n\n\n--- Top Comments ---\n\n\n[46 upvotes] Just comenting To support research , The vram peasants are gratefull for your work  \n(It woul be nice to know the kv quant , how much context would you be able to fit and how many prompts or token are taken as sample on. Each model )\n\n[18 upvotes] Here's a quick visualization, thank you for your work!\n\nhttps://preview.redd.it/kealh9eb9knh1.png?width=2079&format=png&auto=webp&s=a53240b7741ab11c1eca54d420b5b535890cf49f\n\n[7 upvotes] Screenshoting the heck out of this 😩",
  "transcript_chars": 3145,
  "ingested_at": "2026-09-05T01:30:03.366806+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 134,
    "upvote_ratio": 0.99,
    "num_comments": 30,
    "author": "Storterald",
    "is_self": true
  }
}