# Qwen3.8 27B Ridge GGUF

URL: https://interfaze.ai/models/empero-aiqwen38-27b-ridge-gguf

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Qwen3.8 27B Ridge GGUF by empero-ai, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Qwen3.8 27B Ridge GGUF | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | 100 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 262.1K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Qwen3.8 27B Ridge GGUF | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/empero-ai/Qwen3.8-27B-Ridge-GGUF)

**Developed by [Empero](https://empero.org)**

A Gated-DeltaNet-aware mixed GGUF of official **[Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)** (`1d4bf0f2`) for [llama.cpp](https://github.com/ggml-org/llama.cpp), Ollama, LM Studio, jan, KoboldCpp, and other stock GGUF runtimes.

This is a quantization of the Qwen3.8-27B checkpoint. Ridge is a probed mix of types written for this architecture: 64 layers = 16 × `(3 × GatedDeltaNet → FFN + 1 × GatedAttn → FFN)`. Generic `IQ2_XS` and UD-IQ2 do not treat GDN state (`ssm_alpha` / `ssm_beta`) or the GDN mixers as first-class. We fixed that.

Nothing was stripped to make the file fit. The native MTP draft head (`blk.64` / `nextn`) stays in the GGUF. Vision is a separate BF16 `mmproj`.

> \[!Note\] This card is about choosing the file and running it. The official capability writeup lives on the **[base model card](https://huggingface.co/Qwen/Qwen3.8-27B)**.

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## Files

The repository is `Qwen3.8-27B-Ridge-GGUF`. Use the exact filenames below when downloading or passing `-m`.

| File | Quant | Size | Notes |
| --- | --- | --- | --- |
| Qwen3.8-27B-Ridge-3.7bpw.gguf | Ridge mix, 3.69 bpw | 11.73 GiB / 12.59 GB | this release — text + native MTP |
| mmproj-Qwen3.8-27B-BF16.gguf | BF16 | 0.87 GiB / 0.93 GB | vision encoder + projector; required for images |

If you only want text, download the Ridge GGUF. Add the `mmproj` for image input.

### What fits on a GPU?

These are practical **weight-size-based estimates**, not a VRAM benchmark. They assume a modest context and leave room for runtime and the KV cache. Image input adds the 0.87 GiB `mmproj`. The native 262k window and the 1M YaRN extension — make KV the dominant cost and may need offload regardless of weight quant.

**Measured:** `Qwen3.8-27B-Ridge-3.7bpw.gguf` fully offloaded to a single **RTX PRO 6000 Blackwell (96 GB)** runs at **~54 tok/s generation, ~130 tok/s prompt** (llama.cpp CUDA, `-ngl 99`, short smoke). One data point on one card, not a sweep — but a 27B at 11.7 GiB is comfortably interactive on a 16–24 GB card at modest context.

| File | Approximate hardware guidance at modest context |
| --- | --- |
| Ridge-3.7bpw | The practical 16 GB starting point; 24 GB is comfortable once you add KV and (optionally) the mmproj. |
| \+ mmproj | Add ~1 GiB. Still a 24 GB card for everyday use. |

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## Recipe

Qwen3.8 is a hybrid: three Gated-DeltaNet layers for every full-attention layer. GDN state is disproportionately sensitive to low-bit quantization, so Ridge holds that path high and spends the saved bits by dropping mid-stack FFN.

**The Gated-DeltaNet state path is Q8\_0.** Mixers are Q4\_K, not IQ2. That is the difference between this file and a flat 2-bit dump of the same model.

Built with llama.cpp `adb55e5`, CUDA, importance matrix on 80 × 512-token chunks (`--process-output`, wikitext + code). MTP tensors are unused during calibration and have **no** imatrix — IQ2/IQ3 on `blk.64` will abort, so the draft head stays Q6\_K.

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## Measured

Same box, same calibration file, `llama-perplexity`, 80 chunks, `-c 512 -b 512`. BF16 is our convert of the same official checkpoint.

| Candidate | Size | BPW | Wiki-style PPL | vs BF16 |
| --- | --- | --- | --- | --- |
| BF16 GGUF (this convert) | 50.89 GiB | 16.00 | 7.15 ± 0.12 | — |
| Ridge-3.7bpw | 11.73 GiB | 3.69 | 7.82 ± 0.14 | +9.3 % |

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## Comparison

Published Hugging Face file sizes as of 2026-08-15. PPL is filled only where we measured the file ourselves.

| File | Publisher | Size | Nominal band | PPL vs this BF16 |
| --- | --- | --- | --- | --- |
| BF16 | this convert | 50.89 GiB | 16 bpw | 7.15 |
| UD-IQ2\_XXS | unsloth | 8.39 GiB | ~2.1 bpw | not measured here (Unsloth quotes 82.5 % top-1 vs BF16) |
| UD-IQ2\_M | unsloth | 9.61 GiB | ~2.4 bpw | not measured |
| IQ2\_XXS | bartowski | 8.75 GiB | ~2.2 bpw | not measured |
| Q3\_K\_S | unsloth | 11.71 GiB | ~3.1 bpw | not measured |
| Ridge-3.7bpw | empero-ai | 11.73 GiB | 3.69 bpw | 7.82 (+9 %) |
| IQ3\_XXS | bartowski | 11.76 GiB | ~2.9 bpw | not measured |
| UD-Q3\_K\_XL | unsloth | 12.52 GiB | ~3.4 bpw | not measured |

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## Quick start

### llama.cpp (`llama-cli`)

Sampling from the official Qwen3.8 card. Thinking is on by default.

```
llama-cli \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  -ngl 99 -n 16384 \
  --temp 1.0 --top-p 0.95 --top-k 20 \
  -p "Explain the design tradeoffs in a Gated-DeltaNet hybrid model."


llama-cli \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  -ngl 99 --reasoning off \
  --temp 0.7 --top-p 0.80 --top-k 20 --presence-penalty 1.5 \
  -p "Say hello in one short sentence."
```

### llama.cpp (`llama-server`)

```
llama-server \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  -c 16384 --port 8080
```

### Ollama

```
ollama run hf.co/empero-ai/Qwen3.8-27B-Ridge-GGUF
```

Or a local Modelfile:

`FROM ./Qwen3.8-27B-Ridge-3.7bpw.gguf PARAMETER temperature 0.7 PARAMETER top_p 0.8 PARAMETER top_k 20`

```
ollama create qwen38-ridge -f Modelfile
ollama run qwen38-ridge
```

### LM Studio / jan / KoboldCpp

Download `Qwen3.8-27B-Ridge-3.7bpw.gguf` and load it. Preserve the embedded Qwen3.8 chat template if the runtime asks you to select one.

### llama.cpp with MTP draft speculation

The Ridge GGUF keeps the native MTP head. Use a recent llama.cpp build that supports `--spec-type draft-mtp`:

```
llama-server \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  --spec-type draft-mtp \
  --spec-draft-n-max 6 \
  -c 16384 --port 8080
```

If your runtime does not support MTP, the file still runs as a normal 27B — you just will not get the draft speedup.

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## Vision (image input)

Download the text GGUF and `mmproj-Qwen3.8-27B-BF16.gguf`.

### llama.cpp (`llama-mtmd-cli`)

```
llama-mtmd-cli \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  --mmproj mmproj-Qwen3.8-27B-BF16.gguf \
  --image ./photo.jpg \
  -p "Describe this image in detail." \
  --temp 0.7 --top-p 0.80 --top-k 20 \
  -c 16384
```

### llama.cpp server

```
llama-server \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  --mmproj mmproj-Qwen3.8-27B-BF16.gguf \
  -c 16384 --port 8080
```

* * *

## Sampling

Qwen3.8 is a hybrid thinking model. Responses open with a `<think>…</think>` block unless thinking is disabled.

| Mode | temperature | top\_p | top\_k | presence\_penalty |
| --- | --- | --- | --- | --- |
| Thinking (default) | 1.0 | 0.95 | 20 | 0.0 |
| Instruct (thinking off) | 0.7 | 0.80 | 20 | 1.5 |

Use the runtime chat/completions path rather than hand-rolling a different prompt format. The embedded template is Qwen3.8's, including tool-use (`<tool_call>…</tool_call>`).

## Long context

Native context is **262,144** tokens, extensible to **1,000,000** with YaRN. Set `-c` to what you actually need — the KV cache, not the 11.7 GiB weights, is what blows up a 16–24 GB card at long context.

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## Limitations

-   **Not lossless.** +9 % wiki-style PPL vs our BF16 convert
-   **Context costs memory.** Weight size is only part of the hardware budget.
-   **MTP is runtime-dependent.** The head is in the file; the speedup needs a runtime that knows `draft-mtp`.

## Stay in the loop

Sign up for the Empero newsletter at **[empero.org](https://empero.org)** for releases, evals, and research notes.

## Support / Donate

If this model helped you, consider supporting the project:

-   **BTC**: `bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v`
-   **LTC**: `ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x`
-   **XMR**: `42Dbm5xg5Nq26fdyzfEU7KBnAJfhi7Cvz5J2ex5CzHXkfKuNEJzYCcmJ1GTbgjFZ5MBx72sdG1G9239Cd6rsZfv4QeDkYJY`

* * *

## Provenance & licensing

Quantization of **[Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)** @ `1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0`. Weights are **Apache-2.0**, inherited from the Qwen base, shared as-is.

## Acknowledgements

-   Developed and released by [Empero](https://empero.org)
-   Base model: [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) (Alibaba Qwen team)
-   GGUF quantization: [llama.cpp](https://github.com/ggml-org/llama.cpp) (ggml-org)

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)