---

base_model: Qwen/Qwen3.8-27B

base_model_relation: quantized

library_name: gguf

pipeline_tag: text-generation

license: apache-2.0

quantized_by: aj9o9

tags:

- gguf

- qwen

- qwen3.8

- llama.cpp

- imatrix

---

Qwen3.8-27B GGUF

GGUF of Qwen/Qwen3.8-27B for llama.cpp.

More files will land here. First one is a role mix I baked for my 3090, not a flat IQ3.

A note from me

Same deal as my Ling-3.0-flash

and Nemotron-3.5-Lightning

uploads: I make these to run on my own box, then share them.

Please report anything you find. Bad output, crashes, wrong metadata, a better

flag for a particular card — open a discussion here or reach me at

@ItsmeAjayKV.

Files

| File | Size | What it is | Status |

|---|---|---|---|

| Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf | 13.6 GB (12.7 GiB) / 3.99 bpw | Role mix. GDN state Q8, attn Q6, mid-FFN IQ3_XXS. See below. | up |

| Qwen3.8-27B-Q4_K_M.gguf | ~16–18 GB | Flat K-quant, if I bake one from the same BF16 | waiting |

| Qwen3.8-27B-Q5_K_M.gguf | ~19–20 GB | Same | waiting |

| Qwen3.8-27B-imatrix-v6.gguf | ~14 MB | Imatrix used for the mix. Reusable. | maybe |

This is not a flat IQ3_XXS. Only the mid-FFN tensors are IQ3_XXS. The filename is the recipe.

Download (CLI)

hf download hf://aj9o9/Qwen3.8-27B-GGUF/Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf

That pulls just the mix, into the current directory. Same thing, older-style:

hf download aj9o9/Qwen3.8-27B-GGUF --include "Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf"

Imatrix (only if you want to requant, not needed to run):

hf download hf://aj9o9/Qwen3.8-27B-GGUF/Qwen3.8-27B-imatrix-v6.gguf

Rule of thumb I actually use: pick the largest quant that fits in RAM/VRAM, not the largest one you can download.

Should you use the mix?

Take this if you want ~13G and you care more about keeping attention / GDN state fat than a uniform 3-bit file.

Skip this if you can hold Unsloth UD-Q3_K_XL (13.4G) or a Q4. Those beat it on every number I ran. I am not going to pretend otherwise.

What the mix actually is

Official BF16 → my my-mix.txt + bartowski's Qwen3.8 calibration-v6.

| Role | Type |

|---|---|

| GDN state (ssm_* except out) | Q8_0 |

| Full attention Q/K/V/O, embed, output, MTP | Q6_K |

| GDN mixers (attn_qkv, attn_gate, ssm_out) | Q4_K |

| FFN edge (layers 0–3, 60–63) | IQ3_S |

| FFN mid (everything else ffn_*) | IQ3_XXS |

| norms | F32 |

Imatrix: bartowski calibration-v6, rendered through this model's chat template. 583 chunks at -c 512, --parse-special --process-output. About 63% of that file is tool-call text. I did not use wiki-only calib.

Needs a recent llama.cpp with qwen35. Old trees will not load it.

Numbers I actually measured

Same box, same llama.cpp, same prompts. Wiki KLD is vs official BF16 logits (wiki.test.raw, n_ctx=512). GLSL / hard is my locked suite, think off, t=0.

| model | size | wiki KLD ↓ | GLSL ↑ | hard |

|---|---:|---:|---:|---:|

| Unsloth Q4_K_M | 16G | 0.015 | 0.946 | 6/6 |

| Unsloth UD-Q3_K_XL | 13.4G | 0.031 | 0.922 | 6/6 |

| bartowski Q3_K_S | 13.7G | 0.070 | 0.892 | 5/6 |

| this mix | 13.6 GB | 0.073 | 0.863 | 5/6 |

| Unsloth Q5_K_M | 19G | 0.006 | 0.855 | 5/6 |

| bartowski IQ3_M | 13.9G | 0.057 | 0.831 | 5/6 |

Read it like this:

• If you have 16G+, take Q4.
• If you want the best 13G-class file I measured, take Unsloth UD-Q3_K_XL, not mine.
• This mix beats bartowski IQ3_M on GLSL (0.863 vs 0.831) at a slightly smaller size, and it beats Unsloth Q5 on GLSL at 6G less. That is the honest reason it exists.
• Hard: I fail lfu_cache. Q4 and UD-Q3 pass all six. I am not going to hide that.
• Tools (single-shot + short agent loops) were a tie. Everyone passed the easy set. I will not claim a tools win.

Wiki KLD is English Wikipedia. The imatrix is chat + tools. Those two will not rank the same, and they didn't.

How I run it

llama-server \
  -m Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf \
  --host 127.0.0.1 --port 8080 \
  -ngl 999 -fa on --jinja \
  -np 1 -t 12 \
  --alias qwen38-27b-gdn8 \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  --spec-type draft-mtp \
  -c 24576

From the Hub:

llama-server \
  --hf-repo aj9o9/Qwen3.8-27B-GGUF \
  --hf-file Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf \
  -ngl 999 -fa on --jinja \
  --spec-type draft-mtp \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  -c 24576

MTP tensors are in the file (Q6_K). --spec-type draft-mtp is optional; it drafts, it does not change quality.

Official sampling from the Qwen card:

• Thinking: temperature=1.0, top_p=0.95, top_k=20
• Instruct / no-think: temperature=0.7, top_p=0.80, top_k=20, presence_penalty=1.5

I graded the table above at t=0, think off, so I could actually compare quants.

Hardware this was made on

| | |

|---|---|

| GPU | RTX 3090 24 GB |

| System RAM | 64 GB |

| Runtime | llama.cpp master, arch qwen35 |

The mix is meant to leave room for context on a 24 GB card. A 16 GB card can load the weights; keep -c honest.

Vision: this GGUF is text weights only. If you want the encoder, grab an mmproj from the official convert or from bartowski/unsloth and pass --mmproj. I have not tested that pairing.

About the model

• 27B dense, hybrid 16 × (3 GDN + 1 full attn), 64 layers + MTP
• hidden 5120, native 262k context
• thinking on by default in the official template

See Qwen/Qwen3.8-27B for the real model card. Their numbers are BF16, not this file.

Links

• Official weights: Qwen/Qwen3.8-27B
• Runtime: ggml-org/llama.cpp
• Calib used for the mix: bartowski Qwen3.8-27B calibration-v6

License

Apache 2.0, same as Qwen/Qwen3.8-27B. LICENSE is in this repo.

Disclaimer

Not affiliated with Alibaba, Qwen, Unsloth, or bartowski. Provided as-is.

Use the official card for intended use, safety, and limitations.