empero-ai/Qwen3.8-27B-Ridge-GGUF

Qwen3.8-27B-Ridge-3.7bpw

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, 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).

Files

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

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.

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.

Measured

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

Comparison

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

Quick start

llama.cpp (llama-cli)

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

# thinking
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."

# instruct (thinking off)
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.

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.

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.

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
• —Base model: Qwen3.8-27B (Alibaba Qwen team)
• —GGUF quantization: llama.cpp (ggml-org)